Utilizing big data has many benefits. These include reducing costs, improving productivity, and informing key decisions. Studies show that Fortune 1000 companies can gain more than $65 million additional net income when increasing their data accessibility by just 10%. 

As the world becomes increasingly digitised, large companies see larger data pools and face growing problems effectively using big data. At this point, what needs to be done is to extract key information hidden in the data by making an accurate analysis – something like looking for a needle in a haystack. Intimidated? Well, you don’t have to be. We will be guiding you through how we used the Hadoop ecosystem to address a specific big data problem.

The problem: Our customers who use portfolio builders create their own financial portfolios by using stock data. This stock data is updated daily by another API. At the beginning of the project, there was no problem as our data size was relatively manageable. However, once we added mutual funds with ETFs, the data size and volume increased. As a result, performance noticeably decreased in the PostgreSQL database. Thus, we thought of trying big data tools to remedy this problem.

For us, using big data as a solution was broken down into 3 parts. First of all, we chose to use the Hadoop Distributed File System (HDFS) as data storage. Secondly, we used Sqoop to transfer the data from PostgreSQL to HDFS. After all the data was ready, we experimented using Hive and HBase with queries.

First step: Solving the Storage Problem

We needed a storage infrastructure designed specifically to store, manage and retrieve massive amounts of data or big data. These big data storage infrastructures enable the storing and sorting of data so that it is easily accessed, used, and processed by applications and services.

HDFS: In Hadoop applications, HDFS is the main data storage system and represents a distributed file system that offers access to application data for high throughput. It is part of the big data environment and offers a way for vast quantities of structured and unstructured data to be handled. To handle the computational load, HDFS distributes the processing of massive data sets over low-cost computer clusters. One thing to bear in mind is that HDFS is not suitable for real-time processing. If you have such a need, the final topic of this article on the HBase database will be of help to you.

We have two tips for using the HDFS system. First of all, spend time understanding the system and become familiar with the data. Following this, it is essential to understand what your company needs and expects from the operation. Once these two check boxes have been ticked, the only thing left is to prepare the necessary environments and move the data to HDFS. Companies usually undergo this shift when they are running batch processing. 

The screen below illustrates a single node cluster configuration for data node and name node data saving. YARN is a major component of Hadoop and allows data to be processed through the various procedures stored in HDFS. As all processes should be tested to make sure they work, we ran the YARN and HDFS systems separately on the platform. Below is an illustration of the process.

Next step: Data Ingestion into New Environment

The next step is to transfer the data to the Hadoop data lake. These transfers can be made in real-time or in batches. 

Sqoop: When you are ready to conduct data analysis, Sqoop helps you transfer the data to the Hadoop environment. Sqoop is an open-source tool that allows you to ingest data from many different databases into HDFS. It also can export data from HDFS back into an external database like Oracle or MSSQL. 

Many companies use a Relatable Database Management System (RDBMS) for daily transactions such as customer movements. This is a sample Sqoop script that we have used to transfer over a 75million records from PostgreSQL to HDFS. This script can be tailored to your company’s needs and can be used for different analyses by transferring newly incoming stock data from any RDBMS database to the Hadoop environment.

You can use the code blog below to transfer your local system data to the Hadoop environment.

Final Step: Performance Comparison 

We tend to use the PostgreSQL database as a structure and we detail here our experiences during some trials. While we did not utilise complex queries, there were still some delays spanning 2-3 seconds to 7-10 seconds. 

Hive:  Hive provides easy, familiar batch processing for Apache Hadoop and uses current Structure Query Language (SQL) competencies to conduct batch queries on data stored in Hadoop. Queries are written using HiveQ, a SQL-like language, and executed via MapReduce or Apache Spark. This makes it easy for more users to process and analyze infinite quantities of data making Hive the most useful for data preparation, ETL, and data mining.

Hive enables companies that have their data files in HDFS to be a significant source of SQL queries. We can leverage Hive to tackle Hadoop data lakes and connect them to BI tools (like OracleBI or Tableau) for visibility.

Here are the steps you need to take to use Hive after uploading the files to HDFS. First of all, you need to create a table. Following this, you will connect the table with the file extension on HDFS. The images below illustrate these two steps. 

After the table has been connected, we can easily filter and pre-process our file on HDFS by accessing it via Hive.

HBase: Apache HBase is a non-relational, column-oriented database management system operating on HDFS and supports jobs via MapReduce. Being column-oriented means that each column in the system is a contiguous unit of page. An HBase column represents an object attribute; if the table stores diagnostic logs from servers in your setting, each row may be a log record, and a regular column may be the timestamp of when the log record was written. The column could also represent the name of the server from which the record originated. HBase also supports other high-level languages for data processing. HBase is suitable for your current process if you don’t need a relational database and require quick access to data.

 

As we mentioned before, HBase does not store files internally. Hence, we need to connect directly to HDFS and transfer the stored files into HBase. You can refer to the sample code blog we have used below to initiate the transfer. Don’t forget to create a table in HBase before doing so.

In HBase, there are no data types; data is stored as byte arrays in the HBase table cells. When the value is stored in the cell, the content or value is distinguished by the timestamp. This means that every cell in the HBase table can contain multiple data versions. In the picture below, you can see how HBase has stored our data. A key assigns values for each column when given a date, and the rows are sorted according to row keys.

Results: When we analyzed the historical data, Hive gave us faster performances. However, when users wanted to see the stock data they were filtering instantly, PostgreSQL was faster here. Hive loses a lot of time preparing to run map-reduce, so it is only used in the historical batch analysis. Thus, it is not suitable for Online Transaction Procession (OLTP). 

Once we tested HBase performance over PostgreSQL, we saw some performance improvement, but it failed to satisfy. When processing a small amount of data, all other nodes are left idle, and only a single node is utilized. Petabytes of data must be stored in this distributed environment to use HBase effectively. Since we do not have such a large data pool and prefer an official SQL structure, we chose not to proceed with the HBase.

Summary

In this walkthrough, we have illustrated how Bambu utilized big data tools to solve a problem we were facing. We hope that this demystifies your impression of big data tools and has given you insight into effectively deploying them. 

We have also shown that there is more than one data processing tool in the Hadoop environment. To determine which tool to use, you need to first look at your data and focus on your problem. When the appropriate big data tool is chosen, data processing is made much more accessible. 

Even though many industries have embraced digital platforms, we see that the wealth management industry is still hesitant to undergo a digital transformation. Forbes has reported on a study that found that only 16% of US and Canadian banks employ fully digital verification tools for their customers to open an account online securely. When considering how the wealth management landscape is changing, this hesitancy in adopting digital platforms is highly concerning. More regulations are being imposed on wealth managers in recent years, giving them less freedom and time to advise clients. Furthermore, clients are becoming increasingly used to digital interaction and expect wealth managers to provide such platforms and opportunities. These conditions result in a growing dissatisfaction amongst clients, which necessitates change. Why is digital transformation in the wealth management industry occurring at a slow pace? Let’s look into the challenges that firms face, causing them to hesitate. 

 

A significant reason for this hesitation comes from the daunting task of cultural transformation. Yann Charraie, Managing Director of One Wealth Place, shares on episode 28 of our podcast how company size can be a considerable factor influencing the adoption of digital technology. Yann believes that digital transformation cannot take place without a cultural shift within the company. Their successful legacy and sedimented methods cause them to be resistant to change for large and established financial institutions. Furthermore, due to the sheer number of subsidiaries large institutions have, it can be challenging to implement a cultural shift across the entire company. This cultural transformation and getting employees on the same page is thus a daunting task that larger companies face, slowing down the pace of digital adoption. Cornerstone has released a report supporting this, highlighting that bank executives do not have a homogenous understanding of digital transformation. Therefore, there are often misconceptions regarding how far along their institutions are when implementing digital solutions. These different levels of understanding result in friction within the company and further slow down the adoption of digital platforms. 

 

Beyond the challenges of cultural transformation, the mindset that financial institutions have has slowed down the adoption of digital platforms. Debbie Watkins, CEO and co-founder of Lucy, shares on episode 19 of WealthTech Unwrapped about how large financial institutions are reluctant to understand their customers’ challenges. This causes them to be stuck in their ways, relying on archaic practices even though their customers seek alternative methods to manage their wealth. 

 

Within this terrain of friction and hesitation, financial institutions can alleviate much of this by partnering up with Fintech firms. While digital transformation is intimidating and challenging, Fintech firms can assist with onboarding the digital platforms, freeing wealth managers up to help their clients. However, some misconceptions about Fintech firms within the industry are dissuading this mutually beneficial partnership. 

 

Misconception 1: Fintechs only work with loans and transactions 

A common misconception about Fintechs is that they only work within the narrow fields of lending and payment. This is far from the truth as Fintechs work in other areas such as investment planning and insurance. Furthermore, Fintechs offer an array of different products and services which can help value-add the operations of financial institutions. 

 

To provide an example, Bambu has partnered with Vestwell, and by leveraging our wealth management APIs, Vestwell can offer personalised investment strategies. This helps their clients better prepare for retirement based on actionable retirement goals that they can work towards. Read more on this partnership here.

 

Misconception 2: Fintechs only influence large markets 

Fintechs have been accused of only targeting large markets such as the US, Europe, or China. While the Fintech scene in these regions is booming, this does not mean that Fintech has no influence elsewhere. On the contrary, Fintech is everywhere and has embedded itself in every aspect of our lives.

 

On an episode of our Wealth Tech Unwrapped podcast, Oscar Decotelli, CEO of DXA invest shares about how he is trying to change the negative perception of South America by enabling his customers to invest in South American companies. Through DXA’s digital platform, everyone can participate and invest in these companies regardless of their level of wealth. This is but one example of the influence that Fintechs can have in markets all over the world.

 

Digital Transformation Made Simple

In addressing these misconceptions, we hope to have shed some light on Fintech as an industry and put some concerns to rest. Collaborating with Fintechs can alleviate many pain points that wealth management firms have when implementing digital technology. When you partner with Bambu, you can leave the tech to us. With numerous projects completed and many satisfied clients, we’ve shown that digital transformation doesn’t have to be that complicated. Contact us at sales@bambu.co to find out how we can help you embark on your digital transformation journey. 

Robo-advisors are very much the new kids on the block in the realm of wealth management. CNB reports how analysts have predicted Robo-advisory to grow into a $1.2 trillion industry by 2024. With many eyes turned towards Robo-advisory, concerns have been raised about the dwindling role of human advisors. Historically, offering financial advice has been left to human advisors. However, discussions around the rise of Robo-advisors and how they might one day replace human advice have resulted in the bifurcation of these two modes of advisory. We believe that this is a false binary, and rather than replacing humans, technology is here to enhance. Hybrid models are the most common model deployed to optimise the quality of advisory services. These models utilise a combination of human and digital capabilities, highlighting how both modes of the advisory can be used to support one another. According to research done by Accenture, there is also user demand as clients prefer using hybrid models to manage their finances. Let us dive into why this is the case and how exactly hybrid models operate. 

 

Hybrid Models – Why you should use them

Today’s hybrid models are characterised by a digital platform used by the client, alongside a human advisor who provides the necessary support and information. Under this model, clients will reach out to their financial advisors for support when facing any difficult financial decisions. Tobias Henry writes in “The WealthTech Book” about how in its most basic form, the hybrid model combines the prime components of human-based advice with digital advice. This harmony offers a flexible and tailored wealth management solution to clients of all demographics. The hybrid model is also highly beneficial for financial advisors as the digital component of this approach increases the advisor’s scalability. With digital technology taking care of the laborious and time-consuming backend work, the financial advisor is now able to attract and serve more clients while maintaining high-quality service.

In an episode of Wealthtech Unwrapped, Sam Beeby shares how hybrid models are imperative in this digital age. Sam notes that standalone Robo-advisors are yet able to offer holistic lifetime advice. As a result, a large proportion of Robo-advisory users are those confident enough to manage their finances. April Rudin, Founder and President of the Rudin Group, supports this when she shares how ultra-high net worth boomers have the highest rate of adoption of digital technology. This is because they are mobile, global, and have sophisticated portfolios. Indeed, not every investor is like this aforementioned demographic, confident enough to manage their finances. Thus, organisations should be focusing on providing this hybrid model, making financial planning more accessible to the masses.

Sam also adds that a hybrid model is best equipped to build a user’s trust in technology. Since we have yet to arrive at a stage where everyone is comfortable with fully trusting Robo-advisory, the presence of the human component is critical. Chuin Ting, CEO of MoneyOwl, pushes this point further by sharing the ethics of technology on our podcast. Ultimately, technology is designed by us and is influenced by human biases, good or bad. As a result, technology itself has specific trust attributes that need to be navigated by both managers and clients. To foster a trusting relationship with technology, the human element in a hybrid model is crucial.

 

Partnerships – Moving Forward

Ultimately, hybrid models bring together the strengths and make up for digital and human advisory weaknesses. Rather than viewing the two forms of advisory in silos, the gamut of positive benefits illustrated here highlight how they should be used in tandem. 

Are you looking to create your own hybrid Robo-advisor platform? With years of experience under our belt, we at Bambu are well equipped to service all of your Robo-advisory needs. Contact us at sales@bambu.co to learn more about how we can help seamlessly integrate Robo-advisory solutions and present your clientele with a fluid hybrid experience. 

Fintech, short for financial technology, has pervaded every aspect of our lives. This union between finance and technology has heavily altered the way banking and finance are being done, revolutionising the way we manage our transactions and assets. What is impressive is that this revolution has become woven so seamlessly into our lives that it feels like just another part of our everyday happenings. However, when we think about it, these daily happenings are greatly influenced by Fintech. Whether it be online banking, sending money digitally to a friend, or even paying for products using our smartphones, Fintech has been steadily making these transactions more convenient. How exactly did Fintech manage to infiltrate so deeply into our lives, and why will it continue to do so? Let us dive in. 

 

A big reason why Fintech has managed to permeate so widely is due to the rapid growth of the industry. All over the world, innovators are working round the clock to remodel our financial services. With his 20 years of experience in Fintech innovation, Rich Turin shares with us on our podcast Wealthtech Unwrapped about the growth of Fintech. He notes that Fintech is a hugely competitive industry in China, similar to how investment banking was like in the West. The lucrative nature of Fintech attracts many young people who are willing to sacrifice to earn more, fueling rapid innovation within the industry. With innovation comes new services and products that continue to simplify our lives. This leads us to our next point, which is that Fintech is global in its reach.

 

In a world without borders, the rapid growth of Fintech in any part of the world will have global ramifications. Rather than influencing their local geographies, Fintech advancements will impact the entire ecosystem surrounding banking and finance. Looking at the new asset class of cryptocurrency, we can exchange our local currency for a more stable digital coin that will generate yield over time. Because cryptocurrency exists in a decentralised space, anyone from anywhere in the world can invest in it. In an interview with Edmund Lowell, founder and CEO of KYC-Chain, he shared that this was especially useful for residents of countries with a collapsing local currency as they will take refuge and protect their savings using cryptocurrency. Since anyone can participate, Fintech as an industry is without borders and can influence almost every corner of the globe. 

 

Finally, Fintech has only been able to become so ubiquitous due to the advances in technology. Historically, the reality has been that financial planning is for those who can afford it. However, advancements in technology have helped lower the cost of financial planning, allowing many more to access this service. One way has been through Robo-advisors. These Robo-advisors are a marriage between human advice and technology, using algorithms to provide automated investment guidance to anyone who uses it. They not only make financial advice accessible but straightforward as well.  Once the deployment of Robo-advisors has become widespread, financial planning will no longer be the prerogative of the affluent.

 

According to Bambu’s Consumer Sentiment Analysis, tech-savvy investors are seeking information and investment options to provide more context. Robo-advisors, because they lower the barrier to entry for financial advice, are well-positioned to meet the needs and demands of these end consumers. Furthermore, decreasing advisor fees using technology is especially crucial during economic downturns. As people’s finances become tighter, they will be less willing to pay a high premium for financial advice. With these benefits above implementing Robo-advisors, many large financial institutions and firms are quick on the uptake. For instance, Deloitte forecasts that by 2025, over $16 Trillion worth of assets will be managed by Robo-advisors. As members of the Fintech community, Bambu aims further to advance the deployment of Robo-advisors among various financial institutions. We help our clients navigate their key considerations to implement digital solutions that value-add to their business, helping them create the best digital wealth experience for their customers. 

 

There is no foreseeable ceiling to the heights that Fintech can reach. As the industry grows and technology progresses, the union between finance and technology will continue to be strengthened. Aki Ranin, the Co-founder of Bambu, shares that Fintech will become so integrated into our lives that managing our finances will become increasingly unconscious for users. He believes that technology will take care of our bills one day, optimise our spending, and help us invest for retirement, all behind the curtains. All in all, Fintech will become increasingly intertwined with our everyday lives.

 

The acquisition of Tradesocio will extend Bambu’s digital wealth capabilities, doubling the number of employees to 130 and further accelerating global expansion.

Singapore, July 13 2021 – Bambu is pleased to announce the acquisition of Tradesocio, a WealthTech company with 65 employees, specialising in investment management and trading technologies with offices in Singapore, India, and Dubai. This acquisition significantly strengthens the combined business’ competitiveness globally. Bambu will have a presence in all major financial hubs and expanded digital wealth capabilities covering stock trading and cryptocurrencies.

Through the acquisition, Tradesocio brings years of experience delivering and operating high-volume trading platforms across various asset classes. The acquisition puts Bambu in a unique position that will provide customers greater agency through broader system capabilities that go beyond the offerings of existing robo advisor platforms. In addition, Tradesocio’s presence across EMEA and India, along with an existing portfolio of clients, is set to further Bambu’s reach in a rapidly expanding and evolving global digital wealth market.

Ned Phillips, CEO of Bambu, said, “After five years of building solid foundations, Bambu is now entering a phase of rapid growth. This deal helps us in three key areas: it expands our product offering into stocks and crypto, it gives us a wider global footprint and enables us to scale our team effectively to match exponential demand. We believe this positions us well for our Series C and ambitions of becoming the global leader in WealthTech.”

This is unlikely to be Bambu’s last acquisition as they foresee acquiring more companies that strengthen their product mix and global reach to impact the digital wealth industry.

 

About Bambu

Bambu is a leading global digital wealth technology provider for financial institutions. We enable companies to make saving and investing simple and intelligent for their clients. The cloud-based platform is powered by our proprietary algorithms and machine learning tools. The company serves over 20 financial institutions globally. Founded in 2016, Bambu is headquartered in Singapore with a subsidiary in the United Kingdom and the United States and EMEA representatives. For more information, visit https://bambu.co/ and follow us on LinkedIn and Twitter.

 

About Tradesocio

Tradesocio provides Digital Technology that helps Financial Investment institutions manage, offer and access secure and profitable financial services. We allow financial institutions to attract a wider clientele, ranging from the retail to the high-net-worth institutional investor, and offer them access to a variety of financial services, bringing equal opportunities to the world. We offer tailored digital investment management solutions to the wider investment management community that are reducing costs and increasing revenue potential.

We provide the complete end-to-end financial management solution, from development, hosting and maintenance, to security and post-sales technical support.

 

Media Contact

Laura Pereira
Senior Marketing Manager, Bambu
laura@bambu.co

 

Who doesn’t want financial stability? A financial cushion that can serve them and be their back during rainy days? A retirement plan that can offer them the satisfaction of having a filled bank account when they are out of work? A surety that they will be able to pay the hospital bills in case of emergency? A guarantee that they will have enough to request a mortgage for their new house? All this requires some financial knowledge.

Financial knowledge comes when people have access to good financial advisors. Sadly, getting honest financial advisors that offer helpful suggestions is like finding a needle in a haystack. That’s why wealth management is so complex. 

In fact, you would be surprised to know that even in the US, less than 25% of people have ever used a financial manager for advice. According to a survey, only 18% of Americans actively seek financial advice, and the biggest reason is that they consider it an extra expense.

Fortunately, digital wealth management platforms are changing that. And powering them is a wealth management API that is trying to improve how wealth management platforms are made today. One of the providers of wealth management API is Bambu.  It is redefining how wealth management works by building better and faster wealth management applications. In addition, this wealth management API contains data that can help wealth management platforms’ clients to invest in their goals.

What is a wealth management API? How Does it Work?

Wealth management has always been a hot topic. But it has become even hotter recently after the global pandemic and market crash in 2020. All this has created more demand for financial advice, and people are now opting for digital investment options. As a bank, an insurance company, an investment service, or a brokerage, this is the best time to create a wealth management robo advisor that can help your clients and a wealth management API can help you build it faster and better.

Consider wealth management API as a solution to your robo advisory platform. For example, suppose you are creating a robo advisory platform that can help your clients manage their portfolios, take care of their retirement plans, and reach their house purchasing goals. In that case, a wealth management API will provide the foundations for that.

With a wealth management API, you won’t have to start from scratch. That means not creating the whole algorithm to calculate investment goals and provide portfolio projections to the clients. Of course, all this takes many people from multiple disciplines (finance, engineering) and time to build. With the API, you will create the basic infrastructure and the wealth management API will take care of all the processing that goes in the backend for crafting financial plans for your clients.

Who Should Use a Wealth Management API?

Anyone who is offering financial management services to their clients or willing to offer them in the future should connect to a wealth management API. 

This could be: 

  • Banks looking to offer financial planning and wealth management services
  • Financial advisors looking to offload their work to automated robo advisors
  • Startups in the financial space looking to launch their product faster
  • Fintech companies looking to add a new module with their current offering
  • Brokerage firms trying to create their wealth management platforms

All these are the ideal users of a wealth management API as it will allow them to create/launch their robo advisory platforms with minimal efforts. 

Bambu Wealth Management API: How Can It Help?

Bambu is one of the leading online wealth management platform providers. It offers wealth management solutions such as  

robo advisory, API so that its clients (financial services) can create wealth management and financial planning solutions for their customers. 

How Does Bambu Provide Financial Projections?

 

Creating a robo advisory platform using the Bambu wealth management API is simple. The API is powered by financial data in multiple countries to enable wealth management providers and an easy way to build goal helpers for their clients.

For example, let’s say you are creating a robo advisory platform for your financial firm. The robo advisory platform can guide clients on a day-to-day basis in planning for:

  • Child’s education
  • Retirement
  • Healthcare

Let’s go through an example of how Bambu wealth management API works in real scenarios. We will use a child’s education goal as an example. Then, we will show some screenshots from a real wealth management platform that uses Bambu API to build a child’s education goal helper.

To analyze and provide an accurate estimate of how much the client should save for their client’s education, the wealth management API will interact with Bambu’s financial data mentioned above. The data will then be combined with an algorithm to project the future amount of the goal. 

In the child’s education goal helper platform below, the client will be asked for the child’s age of education, the university’s location, whether or not the university is public, and whether or not the major is medical.

After the client fills in all the required info, the platform will send a request to Bambu API and the API will then send a response containing the estimation of the child’s education amount, which is $129, 874.

How Can You Start Using Bambu Wealth Management API?

Go to developer.bambu.co to get started. You can create a free account and try the API from there. The website contains tutorials to get started using the API to build multiple-goal helpers such as a child’s education, retirement, and house. The free tier gives you a 5000 API calls quota per month. 

What’s Next?

If you are looking to accelerate creating your wealth management platform that can guide your clients in planning their investment goals, then Bambu wealth management API offers all the help you would need. Try the API today and see how it can help you create your wealth management platform faster and better.

Our engineering team is a group of passionate and talented individuals who work hard and play hard.

What technologies do they use? 

The main tech stack the team uses is JavaScript-driven, leveraging ReactJS in the front-end and NodeJS in the back-end. This allows for cross-functional capabilities and interoperability throughout the stack. Javascript is also well represented in terms of the talent pool and more than capable of serving most web application needs today. For more computationally heavy tasks, the ream relies on technology like python for its wide availability of libraries or C# for better performance.

The application is entirely containerized through docker use, allowing the team to deploy quickly in different systems or provide a local testing/development environment that any developer/tester/product manager can quickly bring up. This helps speed up the software development life cycle, which enables them to create working prototypes much faster.

The team has decided on AWS as the infrastructure of choice due to its broad global market penetration for availability and in-house expertise. Being on a matured and established cloud platform allows them to focus more on the product rather than spending time optimizing the infrastructure. Due to the containerized nature of our application, ECS (Elastic Container Service) and EKS (Elastic Kubernetes Service) are primarily used and managed because they allow for easy scalability, high availability, and disaster recovery. The infrastructure deployment also follows the principle of infrastructure as code by using AWS Cloud Development Kit (AWS CDK). This allows the team to migrate and create new environments easily in a different region whenever needed. 

How do they accomplish their daily activities?

As a part of a cross-functional development team, the engineers will participate in a sprint. A sprint is a time-boxed period during which the team needs to complete a set amount of objectives. 

In every sprint, a lead engineer will understand the big picture and break down stories into tasks that other engineers can pick up. A task will have a story point, usually representing the number of days an engineer may require to complete the task. Once an engineer is done with their task, they will be required to make a pull request (code review request) to their fellow engineers. Pull requests will serve as a feature for engineers to discuss and provide feedback on the implemented task. It can also be leveraged to modify the task if necessary. Once a pull request is approved, the task will be marked as ready to be shipped for quality assurance testing. 

The sprint will end with a review, serving as an opportunity for engineers to showcase their completed tasks to the entire team.

What’s our software development process?

The methodology followed within Bambu is the Hybrid Agile model, which combines Agile methods and other non-Agile techniques, such as the Waterfall model. It is often considered an intelligent approach adopting Agile-Waterfall methodologies as this method is able to retain the clarity and maintainability of the Waterfall method while embracing the adaptability and flexibility of Agile.

The first step: Requirements Analysis Phase. 

User expectations for a new/ modified product are determined. This involves recording the needs of the clients and conducting analysis to ensure clarity and completeness of the discussed requirements.

 

The second step: Design Phase. 

A high-level design schematic is crafted and signed off by the client’s business stakeholders to ensure the designs are aligned with the given requirements.

 

The third step: Implementation Phase (coding). 

Developers are provided with the approved design schematics. The software design is translated into efficient source code.

 

The fourth step: Testing and Documentation Phase. 

The test plan execution is done in each sprint to minimize the risk of failures. Performance of tests will ensure that the product performs as expected.

The fifth step: Maintained Separation Phase

Separate logical environments for system development, testing, and production are maintained so that no single individual can move object codes. 

 

The sixth step: Deployment Phase

After the project team completes product testing, the product is ready to go live. Change management and incident response plans are crafted.

 

The seventh step: Maintenance and Support Phase

The application system is monitored to ensure data integrity and efficient performance, faults are identified and rectified. 

 

The eighth step: Maintenance Phase

This stage consists of completing change requests, technical support and resolution, and tracking open issues on the systems deployed to production.

Devs hard at work – Photo taken Pre-Covid

 

Being able to achieve success of such high caliber requires skills, patience, and resilience. We applaud our software engineering team for their triumphs! For more insights into Bambu, you can also read “Uncovered Success – The Story of Bambu”, where we interview our CEO, Ned Philips. 

This post is part of an ongoing series of research updates by Bambu, a financial technology company I started in 2016. The theme of these posts is predicting the future. You can find part one here, sharing the results of a survey we ran about people’s attitude towards their future.

So, you think I’m kidding about predicting the future? Just hear me out. Predicting the future is not only possible but even simple if you stack the probabilities on your side by making precise statements about the object and time of the prediction.

Example 1: I predict everyone alive today will die. Certainly, there’s a non-zero chance I’m wrong, but historically that seems a pretty safe bet.

Example 2: Similarly, I can predict that for the next two seconds, you will continue to read this article, or at least finish this sentence. Got you!

So clearly you can predict many things as a party trick, by picking the right granularity of events and time horizon for the prediction.

The question is, where is the line between what’s defensible mathematically, and what’s actually new information that’s useful? If you’re too conservative, you end up with tautologies, i.e. statements that are obviously true but add no value or information besides a tired chuckle from the audience. If you’re too aggressive, then you’ll end up with highly interesting information that simply has no connection to reality, or at best is just a coin toss, and get dismissed as a charlatan.

Is there a sweet spot in between? Well, that’s what we’re going to find out! To help us digest and compare various approaches, I’m introducing a simple chart, that splits into four quadrants on the basis of the aforementioned concepts of utility and relevance.

Historical approaches to predicting the future

It should come as no surprise to you that we’ve been doing this as far as history can shine a light. Whether reading weather, entrails, stars, or palms, man has always sought to interpret what nature has in store for us. Why? Well, nature has always been a cruel master, and perhaps hope placed in omens is better than no hope at all. On the other hand, the randomness of events around us has always been tough to accept, and we chose to believe in divine intervention or deterministic fate to fill gaping holes in our understanding of natural phenomena. Either way, when we didn’t yet have things down to a science, we simply made up weird and random ways to predict until new science came along to show us the way forward.

Shamans

Basis: wisdom, intuition, subconscious
Content: high utility, low relevance

Possibly the oldest form of predicting the future is the tribal shaman. There are historical sources from archaeology to show that this was a common practice amongst many ancient cultures, from the Native American “Medicine Man” to the Rishis of prehistoric India. These cultures relied heavily on the priest class, or even individual members of the tribe, that often had secretive rituals to access the wisdom of the ancestors.

An illustration from 17th century Russia showing a village Shaman in action. Source: Wikipedia.

Often these rituals would include mental practices such as meditation, ascetic practices such as fasting, and even medication in the form of hallucinogens derived from herbs or mushrooms. Given recent research into meditation and psychedelics and their applications in the treatment of various psychological disorders, it doesn’t seem too far fetched to say these shamans may have accessed genuine latent information through altered states of consciousness.

The problem with these predictions is that there was a high degree of subjective interpretation, whether reading pig entrails or describing psychedelic dreams, it left a lot to the imagination. Thus life-and-death questions about when to pick the year’s harvest, or whether to attack a neighbouring tribe were based on rather suspect information, resulting in a morbid and fateful toss of the coin.

Oracles

Basis: wisdom, intuition, history
Content: high utility, medium relevance

One might think of the Oracle as a more refined, institutionalized version of the Shaman. While the term originates from Ancient Greece, we might include the Egyptian priests in this class given their similarities. While very little is documented for us to know about their specific practices and methodology, there are some second-hand accounts in Greek literature. What is clear is that the interpretation is still a part of the show, but the expectations have evolved from outright miracles to sage advice to the rulers of the day. In that regard, Oracles could be seen as a precursor to the specialist advisory teams that surround modern-day world leaders. Just with a little extra fanfare and mystique.

Astrology

Basis: positions of planets and stars at specific times
Content: medium utility, no relevance

I won’t spend much time here, rather than point out that the futility of “reading the stars” is well refuted by science. Frankly, the existence of twins without identical life paths is already a big problem for the methodology. Besides, astrology already scores lower on utility, as it’s relegated more to gossip-column level speculation about your love life.

Astrological birth chart for Emperor Nero of Rome. Source: Wikimedia

Fortune tellers

Basis: psychology, common sense
Content: medium utility, low relevance

The only difference between fortune-telling and astrology is that fortune-telling actually does have some scientific basis. No, it’s not your palm or the cards. Fundamentally, the fortune teller is reading you as a person. The way in which you respond to questions, your choice of words, your emotional state, your body language, and your appearance.

So in some sense, using some homespun psychology and simple common sense, a good fortune teller could be more beneficial than a bad psychiatrist! The fortune-teller has no new information to give, but they can tease out subtle truths that you refuse to acknowledge due to your own biases and emotional charge. Then again, it’s equally possible the fortune teller is specifically taking advantage of your biases to tell you precisely what you want to hear, reinforcing bad decisions. So maybe don’t cancel your shrink just yet.

Current approaches to predicting the future

Moving forward from things that don’t actually work, we arrive in the domain of modern science and mathematics. Let’s examine what can be done with the best of today’s technologies?

The Human Brain

Basis: sensory data, memory, hormones
Content: low utility, high relevance

Didn’t see this one coming, did you? Well, as it turns out, none of us would be around if we weren’t pretty good at predicting the future. Let me explain. There are actually several ways in which we predict the future inside our brain, that we know of, at least.

Firstly, think of catching a ball with your hand. The only reason you can catch it is that your eyes and hands collaborate, facilitated by the brain, to predict where the ball is going to be at a specific moment in time. We forget how hard such a thing is until we have to create it ourselves with robots. Robots suck at picking things up, let alone playing catch with dad.

One such mechanism in our brain that allows this is called anticipatory timing. Without it, you couldn’t walk, because you need to (mostly subconsciously) predict a complicated series of events to know how body mechanics, friction, and gravity will impact whether you slip or faceplant. This explains why babies do a lot of slipping and faceplanting, as they refine their motor skills algorithms just like a miniature robot.

Source: Darici, O., Temeltas, H. & Kuo, A.D. Anticipatory Control of Momentum for Bipedal Walking on Uneven Terrain. Sci Rep 10, 540 (2020). https://doi.org/10.1038/s41598-019-57156-6

The second kind of prediction feels pretty useless, almost harmful sometimes. It’s our ability to imagine potential scenarios or ruminate on them endlessly lying in bed at night. What happens if you miss the bus in the morning? Why did I drop the glass earlier? What could have been if you went on that second date after all? We can conjure up entire lifetimes of complicated events with real or imaginary people in them.

There are other types of prediction happening in our brains, such as from how our eyes and brain together interpret the world around us based on what we want to see. The prediction seems to be a fundamental capability, and we may have only scratched the surface of what’s really going on with things like consciousness. I’ve covered that whole chestnut before, so bookmark that post for later.

CIA “Super Forecasters”

Basis: data, intuition, common sense
Content: high utility, medium relevance

Following on from our ability to rationalize and simulate different scenarios in our mind, it’s logical to think that there might be certain innate abilities or skill development that determine how good or bad one is at this type of thinking. In fact, in 2011, the CIA tried to answer that very question. They set up a competition of sorts, to pick out people from different walks of life to go up against their best analysts.

To set the scene for context, what an intelligence analyst would do to predict outcomes like presidential voting in Venezuela, or chance of war in the Middle East next year, is build models. Lots of models. The models would be based largely on existing data, either public or extracted by the CIA themselves, and try to extract various statistical metrics, relying on rational hypotheses, to judge the overall probability of specific outcomes. The problem is, that there’s a lot of human judgement in that process, in terms of picking good reference data, the treatment of data, framing hypotheses, and the metrics used to extract predictions.

Shockingly at the time, the hobbyist “Super Forecasters” beat the analysts. It wasn’t even close. There’s quite a lot of good material and podcasts on the topic if interested, but the short of it is this. These people had much simpler models than the analysts, who had the best tech and large teams of mathematicians.

Instead, the hobbyists had better intuition about choosing good reference data, and most importantly, they were focused on the art of being a good forecaster. In some sense, they weren’t overthinking the problems, but we’re very focused on a consistent process honing their prediction skills, and that frequently resulted in better results. Mind you, we aren’t talking 90% accuracy. More like going from 51% to 55%, so overall, just marginally better than tossing a coin!

More recently, we’ve seen the very same phenomenon as hobbyists have regularly outscored government agencies in predicting the spread of COVID-19.

Insurance Actuaries

Basis: data, logic, common sense
Content: medium utility, high relevance

Isn’t it peculiar, that the most boring industry is probably the best at predicting the future? That seems like a cool ability, possibly justifying the use of a cape. Well, the issue is that the scope of those predictions is extremely narrow. Hence this work is carried out in cubicles, not secret underground lairs. By definition, insurers only focus on things that drive risk, because they have to foot the bills if they get it wrong.

Here’s an example of how actuarial tables can predict whether people of various ages (x-axis) will live another 5 to 25 years. It’s interesting to note how the curves slow down if you make it past 75. Source: Wikipedia

While the obvious datasets relate to things like health and longevity that determine how much we pay for health and life insurance premiums, the origin story of these predictions was insuring ocean voyages for ships. Sadly, it was quite common for ships to not return, and so money was put up almost as a bet against success, which would then be paid out to widows of sailors and the owner of the ship. Nowadays, more complex models can calculate probabilities for your iPhone screen cracking, or your flight getting delayed.

Fundamentally, the probability is a prediction. Insurers literally bet your life on those odds.

Hedge Fund Quants

Basis: data, logic, common sense
Content: medium utility, medium relevance

Besides insurers, hedge funds also make money with predictions. In fact, that’s the only thing they care about. They purely focus on maximizing investment returns, by any means necessary, while mostly staying within legal and regulatory limitations. As far as we can know, that is. The methods vary, but fundamentally these companies are trying to find an information edge.

At some point, for example, hedge funds were paying for satellite imagery to calculate precise crop yields and count cars in parking lots of shopping malls. Why? Because they wanted to know information on how various commodities and businesses were performing before that data was published in the next quarterly reports. If you knew in advance, you could make a high probability bet on the asset going up or down when that data became public. Yes, it’s effectively legalized cheating.

While making lots of money is useful, that is a pretty narrow use-case. Knowing the price of copper tomorrow won’t help with your life problems, other than that you’ll have more money to spend on worrying about them.

Artificial Intelligence

Basis: data alone
Content: medium utility, high relevance

In case you weren’t aware, the Artificial Neural Networks powering most modern A.I. solutions were directly inspired by the brain. While we don’t understand the brain fully, or particularly well, we’ve had good progress in using a few basic building blocks to train computers in learning useful tasks. This began with identifying handwriting in the nineties and has evolved into beating humans at games like Chess and Go and driving autonomous cars in urban traffic.

Much like the human brain, we covered earlier, some applications of decision making involve the ability to predict the future. Driving cars is pretty self-explanatory in that sense. Firstly, the A.I. needs to know what various systems of the car are doing every millisecond, and how they will coordinate something like an emergency braking situation on an icy road, should that be necessary. But even more complicated is the requirement to predict what humans will do. Remember, other cars are being driven by erratic, emotional hairless apes, us. Not to mention pedestrians doing random things like jaywalking. Don’t get me started on cyclists.

No wonder Tesla’s Autopilot is one of the most powerful A.I. systems on the planet. It comes with 8 cameras giving it a complete 360-degree view, plus sonar. Yes, like whales. The computer hardware running their own neural nets is also now built by Tesla from scratch. Most importantly though, is the data. That explains why they continue to dominate the category, as no one else has a decade’s worth of on-road data from thousands of cars across billions of miles. No data, no A.I., no self-driving. It’s that simple. Read more about all that stuff here.

Board games are another good example because a key part of successful gameplay is to know what your opponent can and will do. Given the latest generation, AlphaZero has a 99% win rate in both Chess and Go against humans, I’d say they’ve got us figured out pretty good. Before it happened, most experts thought it might never happen. That’s how big of a deal it was when Deepmind crushed humanity’s last hope in Lee Sedol in 2016.

Visualizations of the decision-making processes of AlphaGo in choosing the next move (orange circle) with numbers indicating overall win probabilities. Source: Silver, David et al. (2016). Mastering the game of Go with deep neural networks and tree search. Nature. 529. 484–489. 10.1038/nature16961.

The thing to take note of here is that A.I. models are purely and entirely based on the data. So it really matters what data is used. Random data will result in no learning, and biased data will result in biased decision-making.

Academic approaches to predicting the future

While the current approaches all have various real-world applications, there are several fields of academic study that examine human populations to understand them. In some sense, the highest level of understanding is predicting, because that gives you a model that functionally represents the data. Seeing is believing, after all.

Social Studies

Basis: data, logic, common sense
Content: high utility, high relevance

I never studied the humanities, so I’ll be brief here. Not only have we studied ourselves in fields like psychology and biology, but there are whole fields of study around human behaviour in groups large and small. While we all think of ourselves as highly individualistic, with free will (*cough*) to do as we please, the reality we also admit to is that our lives are largely dictated by routines. We all eat and sleep, and some of our work in between those mandatory activities.

When we look at populations of hundreds or thousands of people in communities, we can start to model, and therefore predict, how people spend their time and when they spend it. The specificity of those models increases when you group people by types of unemployment say, shift workers. Pretty quickly, and somewhat unnervingly, I might be able to tell with high accuracy what you’re doing at any given time of the day, between doing laundry and watching TV. The only question is, is that useful to know or just creepy?

Population studies

Basis: data, logic, common sense
Content: high utility, high relevance

If we move up to a higher level of examination, we can look at how portions of the population behave over their lifetimes. It’s just another way to fix our parameters to make it feasible to predict: what are the questions and over what time period. For example, it will be impossible to accurately predict if you will die tomorrow, or not. There are probabilities for that kind of thing as we discussed with insurers, but those are based on other people, not you specifically. Whereas, with those same models, I can pretty precisely predict which decade you will die in. Again, picking the right battle is a big part of the challenge here.

Sociological research is interested in the mechanics and causality of how populations evolve. Think of questions such as what happens to kids from divorced parents, are they more likely to divorce themselves? What about kids from low-income families, how likely are they to go to college? This is where it gets interesting.

One of the methods to tease out these kinds of questions is fundamentally the same technique used under the hood of AlphaGo. Yes, we are playing the game of life with real people. So-called Hidden Markov Models to take sequential datasets, such as moves on a board, or events in real life, and model the relationships between them. These relationships are modelled in complex graphs, which give you the probabilities of the next move or next life event. Different states of the graph model different events that can happen each with a probability adding up to 100%. Between moves, which can be sequential in board games, but years in life course data, there is also a probability of moving to a different state.

With AlphaGo, such graphs represent the rules and flow of the board game. Some parts of the graph (much much larger than above) might represent opening sequences or final moves to find a checkmate. With life-course data, the same graphs represent… the rules of life. How things actually play out. Who gets married, who divorced. Which one of the two has kids, which has none. Even things like who retires earlier, the married or the divorced, or those who stayed single. Most importantly, these events are not disconnected but form a continuum of life events that we can examine from start to finish, allowing us to predict forward from any given scenario. That seems pretty useful!

Global studies

Basis: data, logic, common sense
Content: high utility, high relevance

Another level up and we’re looking at what happens on a global scale. Things like climate change come to mind. Techniques tend to be traditional data gathering and analysis, but as we know from climate change denialists, the science doesn’t often speak for itself.

Another more pressing example is the spread of COVID-19. Below is an animation put together by the World Economic Forum, that shows the first three months of spread day-by-day. It highlights exponential growth dynamics, as in two weeks in March cases ballooned everywhere at once like a bad case of the measles had attacked the map of the world.

More broadly, global predictions are very useful as evidence for policymaking but have relatively little to say about individuals.

Theoretical approaches to predicting the future

If we allow ourselves to take a few steps beyond what is already here, we can look at a few sci-fi inspired methods, including where A.I. could take us eventually. Because… why not? It might show us what’ll be possible soon, as it has many times before. Chances are if you take any modern technology, someone thought and wrote about it fifty years ago.

Mathematics (“Foundation”)

Basis: data, logic, common sense
Content: high utility, high relevance

If you’re not already familiar with Foundation, you will be, as a high-profile TV version streams soon on Apple TV. It is considered one of the great sci-fi works of the 20th century, from the godfather of sci-fi Isaac Asimov. The premise of the original trilogy is a theory of “psychohistory”, which is a mathematical formulation of how humans behave at macro scales. Think planetary or galactic studies, rather than mere global studies. Statistical biases and trends from trillions, not millions of people.

“Since emotions are few and reasons many, the behavior or a crowd can be more easily predicted than the behavior of one person can. And that, in turn, means that if laws are to be developed that enable the current of history to be predicted, then one must deal with large populations, the larger the better.”
― Isaac Asimov, Robots and Empire

Without giving away the plot, one of the fun aspects of this approach is that it only holds true if the predictions are not made public. If people know the prediction, they will change their natural behaviour, thus affecting the course of history.

While nothing like psychohistory exists as a serious field of study, our most recent pandemic is a wake-up call, that a better model is needed to understand certain aspects of global dynamics. Such models could be used to enforce both more reasonable and effective strategies against the next virus. Also, fun fact, Elon has named Foundation as one of his greatest influences. He also seems to be doing things about the future. Maybe he’s figured out psychohistory alongside cryptocurrencies…

Quantum Mechanics (“Devs”)

Basis: data, logic, common sense
Content: high utility, high relevance

Few technologies have sparked the imagination, and utter confusion, of people like quantum mechanics. At least when Einstein rocked the foundations of science with his relativity theories, we could use analogies and images to make sense of it all. We can feel and see gravity all around us, giving us some useful intuitions to start from. We can’t see any quantum events though. If relativity was shocking, then quantum mechanics is just… strange. Even worse, while there are few open questions at the edges of relativity, we haven’t made any meaningful progress on core questions in quantum mechanics since.. well, since it was conceived by Einstein, Bohr, and others nearly 100 years ago.

One of these core questions is whether the universe itself is fundamentally random or not. There are a few broad camps, starting with determinists who believe randomness is just an illusion and everything can still be calculated precisely in a classical sense. We just need to know the underlying “wave function” that generates these illusions. Meaning if we have all the information about a system, we could use quantum mechanics to not only predict forward, but play it back indefinitely.

Others believe there is fundamental randomness where the same mysterious wave function collapses seeming alternative paths into a singular choice, implying somehow that “god throws dice”, which Einstein didn’t like very much. Most bizarrely, there is a third, equally serious camp that believes the universe splits in two with every random event. That both things happen, but… in separate worlds. No doubt something Dr Strange will explain to us in his upcoming Marvel sequel.

Here’s a short video that displays one of the only macroscopic visual experiments that show quantum mechanical properties, including isolation of a “pilot wave” that pushes the water droplet along the surface.

The TV show “Devs” does a pretty decent job playing around with these concepts, tempting us with the prospect of knowing all. One thing is clear though. No scientist alive thinks it’s possible to collect ALL information about any system because that implies measurement which itself affects the system, as dictated by Heisenberg’s uncertainty principle. So the whole thing breaks apart before you can get to anything useful at macroscopic human scales. So don’t hold out on this one happening.

Superintelligence (”Westworld”)

Basis: data, logic, common sense
Content: high utility, high relevance

The last stop on this particular tour puts it all together. We know current A.I. systems can be incredibly accurate at predicting specialized limited scenarios. We do not, however, yet know how to vastly expand those abilities into General Artificial Intelligence, which is more like a human brain works. We can learn one skill in one domain, and naturally, apply it across new skills and even new domains. Kids who are good at one sport tend to be good at all sports. Einstein famously used playing the violin as a way to engage deeply with abstract mathematical concepts. We don’t know how that works.

But what if we did? It would open doors we didn’t even know existed. It almost goes without saying that such as system capable of human-level thinking would achieve superhuman abilities in thinking. Even if you had exactly human brain functionality, you could still extend it with near infinite computation. While that story could go in many directions, most quite extreme, the portrayal of such a superintelligence in Westworld’s season 3 was quite effective.

Again without spoiling much, the A.I. had a lot of data on a lot of things, and much intelligence to ponder on it. The result was an ability to not only predict outcomes on both individual and global levels but therefore actively interject. A kind of manifest destiny powered by computation.

Today, the rapid ongoing advances in A.I. research promise to both deepen and broaden the scope of predictions that can be made on existing data. Most of the people doing that research are saying it’s only a matter of time before we approach true superintelligence, with many pointing to mere decades until it arrives. That means some of us will be around to watch the birth of something that might truly know our next move before we even think of it. Possibly all the other moves after that, too. That makes our future beyond such a point beyond prediction itself, as we hand the yoke of our destiny into the hands of a higher being. Read more about such a technological singularity here.

Can we actually predict the future, then?

So, where does all that leave us in terms of predicting the future? That’s what we’re here for. Well, if we look at the prime candidates, there is one thing in common. While the universe may never bow to quantum information or pure mathematics, probabilistic models based on lots of data already work in many scenarios. There’s little reason to assume that won’t continue to improve quickly, and the limits are yet unknown.

Simple problems: MAYBE

The past few years have seen an explosion in the A.I. community, in terms of availability and open-sourcing of powerful models for a wide range of data and problems. Whether you’re working with time-series, image, video, or language data, there are tools you can try on your data. Really, the deciding factor on whether you can predict accurately is going to be the quality of your data. This cannot be emphasized enough. Some math and coding skill required, for now. Leave the cutting edge stuff to the tech giants though, as the price of entry is going up with bigger models that require bigger datasets and much bigger hardware. All of which is very expensive.

Individual life events: YES

Moving on from simple datasets, a further type of complexity is involved in structuring life events. Generally, this class of problems can be described as gameplay scenarios, and can be solved with A.I. methods such as Hiden Markov Models and Reinforcement Learning developed to make humans obsolete as opponents in board games like Chess, Shogi, and Go. Some day, only Wall-E will get a driving license, and we’ll be passengers from there.

As with the previous category, it comes down to the availability of data. Not just any data, but specifically life course histories. Luckily, at least for developed countries, such data is compiled by academia and government in the form of longitudinal studies following a large group of individuals over several decades. Developing countries, not so much.

Population trends: YES

What we can do for individuals applies even more so for populations, and is already widely used in academia, government, and policymaking. Further, insurance models apply very well at this level, where health and life outcomes can be predicted based on group characteristics. This is the basis for the prices we all pay for health and life insurance, regardless of where you live.

Global statistics: YES

While the weather continues to elude us, due to the chaotic nature of how climates react locally to moment-by-moment conditions, there is an increasing number of things we have a better grip on. Yes, COVID-19 wasn’t a good showing, but it also highlighted how much technology is at our disposal if used properly. You can be sure that we’ll be better prepared for the next one. Similarly, climate change models increase in their predictive power, pointing out the decision boundaries policymakers will have to contend with in managing quality of life, productivity, and sustainability.

Stay tuned for the next one, where we’ll dive into individual life event predictions in the next post in much more detail, as this is the ideal basis for financial planning, the specific topic of interest for Bambu’s research.

The author, Aki Ranin is the founder of two startups, based in Singapore. Bambu is a Fintech company that provides digital wealth solutions for financial services companies. Healthzilla is a health-tech company and creator of the Healthzilla health analytics app for iOS and Android smartphones.

We recently ran a question poll at Bambu to understand how people think about the future. We all have our personal intuitions about the future, of course, but does everyone feel the same?

Well, we asked 138 mostly random people from around the world and found out many fascinating things that largely went against known stereotypes and our intuitions.

NOTE: This wasn’t intended as an academic study to be published, so forgive any statistical or methodological oversight on my part. This was for user research, and helpful as such. Oh, and why is Bambu interested in the future, to begin with? Well, we’re trying to predict it. No, I’m not joking. But more on that some other time…

We think of the future as the next few years

Without getting too pedantic, I think it makes sense to first establish if we’re talking about the same thing. After all, the future is constantly happening to us at different timescales. It also depends on which future you think about, such as the future of the world or the human race. But here, we’re asking about your future. For the sake of the questionnaire, it’s helpful to see most people agree on a timescale of several years.

People think about the future more than you’d think

Well, since it seems we all think about the future a lot, I suppose it’s natural to assume other people do too. But people are weird like that, we all think we’re special little flowers that have interesting ideas and thoughts.

Interestingly, here we see some clear differences between the genders. Only one in four men think about the future every single day, but 37% of females do exactly that.

I got money on my mind

I suppose it’s the reality of western capitalism that we all worry about money. The questionnaire did allow respondents to enter their own answers. Nobody added happiness, which is a little sad, but equally telling that regular people are grounded in regular realities of a living wage, however big or small that wage.

In some sense, it’s nice to run little experiments like this, that are anonymous, because you see the pretences drop immediately. Ain’t nobody out here healing the world, it turns out.

While the #1 goal across generations is getting by, young people seemed less focused on opportunities (12.5% of respondents), which I find counter-intuitive. Instead, they are all about affordability. Older folks seem quite focused on opportunities with that being a priority for 36% of respondents.

We’re scared of living

Now for the juicy stuff. The gossip rag material. I suppose we shouldn’t be surprised our main concern is how long we have on this Earth. So we can spend more time worrying about money, I guess.

Asians were somewhat more concerned about wealth (17.6% of respondents) and marriage (13.5% of respondents), and generally more curious about the future with only 9.5% of respondents not wishing to know a thing.

Young people below 40 cared more about marriage (14.2% of respondents), while those above were more concerned about losing their jobs (8.0% of respondents). The older group was also much more inclined to put their head in the sands of tomorrow, and not know what’s in store at 24.0% of respondents. Above the age of 50, the dominant question becomes longevity at a fitting 50% of respondents.

Somewhat surprisingly, things like illness, retirement barely even register on the scales! After all, those are the questions the wealth management industry is built upon.

We’re scared of dying, too

Isn’t it peculiar, that we are dying to know how long we’ve got, but it’s also the answer we’re most afraid of? Ah, the human condition, what a beautiful and terrible thing it is.

Again, paradoxically, it’s the youngest below 30 that seem most insecure about the future despite all the time they have. They worried more about unemployment (13% of respondents) and their financial prospects (8.7% of respondents). The most confident group was the working professionals at 30–40, who really only feared longevity, but were keener to know everything else at 33.3% of respondents.

How sensitive is information about your future?

This is obviously more self-serving towards what Bambu is doing, but nevertheless, in the age of GDPR and Cambridge Analytica, the stereotype is that people don’t want to share data. Meanwhile, people are sharing their private lives on Instagram like there’s no tomorrow, and accepting terms & conditions for the latest cool apps without even casually glancing through them. We consider data privacy a value, but we all know that values are just things we hope other people do. Cause I need to get on Clubhouse real quick.

Somehow it seems men are more gung ho about sharing data (50% of respondents would share everything, and 27% only specific events), whereas women are a lot pickier (50% of respondents would share only specific events, just 23% all data). Surprisingly, despite their reputation as privacy nerds, Europeans at large were also more likely to share all data compared to Asia.

We’d like to have a crystal ball and three questions

Knowing everything was only the third choice. For some reason, perhaps from cultural influence, we’d like an Oracle instead. No, not the database kind, the Greek kind. Notice there’s a 0.7% hope for humanity. Shout out to your lone soul, seeking happiness, while we’re out cashing checks.

Would you believe your future, even if I told you?

It’s a fascinating question, of course. While you may dismiss it, fortune telling is still big business in 2021. You find palm reading services on the streets of Manhattan and more so online in every language spoken on the planet. Clearly, a scientific basis isn’t a big requirement there. Yet as a more serious undertaking, that would seem to convince people to take my advice over the madam down the street. I see great opportunity ahead, but you must be cautious…

Asians seem less concerned about the scientific basis comparatively (32.4% of respondents), and more focused on detailed events including finances.

We would still act on inside information

If you get a hot tip, even from a guy who knows a guy, it’s human nature to see the cards. You just have to. For us at Bambu, this is very important. Because it means at the end of it all, people need to know, even if they kind of doesn’t. But they really do. People be people.

People in the autumn of their lives were more likely to ignore it all at 10% of respondents, but even there the vast majority would try and make a difference.

So… what’s next?

One can’t write about the future without saying something about the future, after all. So here’s a sneak preview.

Singapore, June 1, 2021 – Vestwell, a digital recordkeeping platform, and Bambu, a global robo-advisory technology provider, are teaming up to provide customers with an even more robust retirement plan experience. By leveraging Bambu’s wealth management API, Vestwell and its partners will be able to offer personalized investment strategies to help their clients better prepare for retirement based on actionable retirement goals.

The new relationship played a role in Vestwell’s recently released advisor managed account offering with Franklin Templeton. Together, they are rolling out an innovative, goals-based offering using Franklin Templeton’s Goals Optimization Engine.

“As workplace investor expectations evolve, it’s vital to deliver participants the types of personalized solutions they’ve become accustomed to in all other aspects of their lives,” said Ben Thomason, EVP of Revenue at Vestwell. “Working with Bambu and Franklin Templeton has made it possible to create a seamlessly data-integrated, low-friction, bespoke managed account experience at a reasonable price.”

Bambu has developed a Wealthtech API proven to provide the information investors need to maximize success for achieving their retirement savings goals. The retirement API has features to cater to US retirees’ needs, which considers Social Security Benefits (SSB), tax, and retirement goals. The investing platform starts with the user’s current status in terms of savings amount and lifestyle needs. Then, the proprietary engine presents investors with an overview of various options, including investment strategies that may be appropriate based on their answers to a targeted risk tolerance questionnaire.

“With the rapidly changing landscape of retirement planning in America, it is important for financial institutions to provide a seamless experience that helps individuals save and plan their future,” said Ned Phillips, Bambu Founder & CEO. “Being able to offer API as an option along with the enterprise and white label solutions has been beneficial. It allows clients, who already have technical resources at their disposal, to build a wealth management platform more quickly by using our APIs for endpoints like retirement goal calculators and portfolio projections.”

Bambu has a library of over 70 Wealthtech APIs designed to make wealth management easy for companies looking to create their robo-advisory platform. These can be categorized by financial planning, country and fund data, machine learning, transactions allocations, and performance monitoring. Bambu provides readily available goal-based wealth management API; no set-up required.