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. 

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.

 

We had the pleasure of sitting down with Varun Sridhar, CEO of Paytm Money, India’s largest online investment and wealth management platform. Varun is an expert in leading financial institutions to digital transformation through intrapreneurship. Prior to Paytm Money, he served as CEO of FinShell India, where he helped launch PaySa, a mobile fintech platform. He was also with BNP Paribas and Deutsche Bank prior to that. 

The interview below has been summarized from an interview we had with Varun in our podcast, WealthTech Unwrapped

Ned (N): How are you doing, Varun? Thank you for joining us. 

Varun (V): Very good, thank you. Super excited to be here. And, you know, I’ve heard of you guys before so I’m very excited that I can add something. It’s a good day today, however, we’re going through some tough times in India. It’s been a challenging few weeks for everyone. But yeah, but I’m happy and safe personally.

N: Happy to hear you’re doing well. There’s a lot we want to cover, so let’s get started. I wonder, how does a corporate guy end up in one of the coolest jobs in FinTech? You’ve been at Deutsche Bank, BNP Paribas, big corporates. Was fintech something you always wanted to do or was it more happenstance that you ended up at Paytm Money?

V: So I think I’ll take a step back and maybe wind my life. I never imagined that I would be in a FinTech. If I go all the way back, I just wanted to be in a bank. I actually bought my first stock at 16. 

You know the reason I wanted to be a banker because my uncle invited me to a five-star hotel in Delhi, and said, “spend as much as you want today, and I’ll take care of the bill.” When a 16-year-old gets an opportunity like that, you think “hey, I want to make a lot of money too”. After graduating in commerce, I was chartered in accountancy and then somehow went into politics. 

So everybody is talking about robo-advisors…

As with any new cool technology, there seems to be many questions or misconceptions around it. Robo-advisors are the future, and therefore it is essential to understand what’s real and what’s just a myth before you decide to build one or use a robo-advisor for your investment needs. 

Here are the top seven myths of robo-advisors you need to know.

Myth #1: Robo-advisors are the same

False! 

They may have similar characteristics such as lower fees, automated transactions, no or low minimum balances and easy account set-up, but robo-advisors differ greatly in many other ways:

  • Types of available investments – such as ETFs, indexes, stocks, gold 
  • Fees or costs charged on your investments 
  • Access or non-access to investment advisors
  • Minimum required initial investment amount ($1 or in the thousands) 
  • Add-on services such as rebalancing or tax loss harvesting
  • Investment strategy
  • User interface

With regard to investment strategy, each robo-advisor tends to have proprietary algorithms that utilize different portfolio optimization techniques. Most use variants of Modern Portfolio Theory (MPT), bootstrapping, Monte Carlo with some perturbations, Meucci’s or Bayesian techniques, Black-Litterman model and plenty more. 

Figure 1 also shows the distribution of portfolio construction methodologies used by robo-advisors. 

To read more about the algorithms behind various robo-advisors, you can refer here and here

RoboAdvisors and portfolio construction

Figure 1: Robo-advisors and portfolio construction
(Source: Which algorithms do robo-advisors use?

Overall, how a robo-advisor is constructed and its functions boils down to the company’s preferred investment strategy. As such, not all robo-advisors are equal. Taking some effort to find out the nuances behind a robo-advisor’s capabilities will help you understand these apps’ different outcomes. This is important when finding the best fit.    

Myth #2: Robo-advisors only offer one-size-fits-all portfolios 

Definitely wrong!

Robo-advisors tailor portfolios according to the user’s goal or risk assessment. The assessment asks questions on a few factors: the time horizon, risk tolerance and amount invested, which quickly but sufficiently frames the user’s needs and financial goals. A mix of investment instruments from the robo-advisor’s investment universe will be selected as part of the constructed portfolio. A moderate risk investor looking to save for a house in the next five years will likely have a very different asset allocation (i.e, investments mix) compared to a low-risk investor looking to save for retirement in 25 years. 

The level of personalization also would vary according to the number of risk bands that a robo-advisor provides. Similar risk levels and goals may result in similar portfolios. Still, such circumstances are arguably inevitable as too many permutations may hinder automation or make it too complex for a robo-advisor. This is also one advantage that human wealth management advisors have over robo-advisors – the level of understanding and personalization of  a portfolio. 

Other than personalizing  portfolios according to goals and risks, certain robo-advisors also provide themed, sector, or idea portfolios for their customers. This is a different kind of personalization that may appeal to customers who prefer investing in things they are more familiar with, or enjoy spotting global trends or growth opportunities. Examples of themes in themed portfolios include shale gas, global recycling, online gaming, environmental social and corporate governance (ESG) and even, a “fight fat” portfolio (investing in multiple weight loss companies).  

See how financial giants have built their robo-advisors and the kind of portfolios here: 8 Big Players in the World of Digital Wealth

Myth #3: Robo-advisors are only for young people

Not at all!

Robo-advisors are a convenient tool for anyone who wants help growing their money. Whether you’re a Millennial, a baby boomer, or part of Generation X, Y or Z, robo-advisors can help you kickstart your investment journey – at any age.

However, we do not deny that Millennials and the generations after them could be a financial jackpot, especially for Registered Investment Advisors who seek to relate to and engage a new generation of clients. 

Find out why this is so: Millennials: The Financial Jackpot for Financial Advisors

Myth #4: I have to choose between a robo-advisor or a human 

Never!

Since both advisors provide different services, they are not mutually exclusive or even exist as competitors. Having one may not and does not need to stop you from engaging another. Consumers should think of both human and robo advisors as tools to achieve the same end goal of discovering their financial needs and to achieve their financial goals. They could also be targeting different segments: robo-advisors appeal to those with a passive investment strategy or with a lower amount of money invested, while human wealth management advisors are preferred by those who invest more aggressively or have a higher amount of money that they want to put in. 

The means to the end may differ – robo-advisors take on more of a passive investment strategy and commonly have lower returns, compared to human advisors. They also cost less than human advisors (human advisors receive 2-3% commissions, compared to less than 1% for robo-advisors). Humans could also be more active in understanding your needs, tailoring your products and monitoring the returns. The higher level of involvement and personalization  is also what drives up cost and requires financial institutions to be more selective with whose portfolios they manage. 

As such, a person may switch between robo-advisors and human advisors or even have both as part of a holistic investment strategy depending on their financial needs. 

Many investment professionals use robo-advising technology as part of their practice — and it works very well for their clients. Research indicates that many investors prefer a hybrid approach and that most clients expect their advisors to use technology to enhance their offerings. 

Myth #5: Robo-advisors are expensive

Nope. 

Robo-advisors usually charge a platform fee that covers transaction and custody fees. On top of that, the underlying financial products include some management fees that are typically collected by the fund manager, not by the robo-advisor. You would pay these same management fees if you were to invest yourself anyway. However, fees can go lower if assets under management are high. Some robo-advisors even remove any platform fee for some of their portfolios, typically those that are low risk and power-saving to attract assets and work on converting them into fee-bearing investment portfolios.

Typically, robo-advisors charge a platform fee of 0.5 to 1% per annum, which is inexpensive given that it covers all trading and custody costs. If you were to use the services of a traditional advisor, you would be likely to pay at least 1% per annum, unless you belong to the high-net-worth and ultra-high-net-worth segments. 

Myth #6: Robo-advisors are not smart and agile enough to weather market volatility

Outrightly false!

You may read that robo-advisors are not smart or agile enough to handle volatility like a Covid-19 crisis. In reality, it is often the contrary. A well-built robo-advisor buys without emotion when markets go down and trim when it goes up. On the other hand, you and your human advisor may base their trading decisions on emotions and do the opposite. Unless you are a financial genius, research shows that it is best to invest as passively as possible.

Myth 7: Robo-advisors are for those who can invest big

Not really.

As we have noted before, you don’t need a high investment amount to invest in a robo-advisor. Robo-advisors are primarily for those who don’t have enough money or the time to do financial planning and investments. If you have a lot of money, you can get your own human advisor, which will often cost you more but may not provide you with the results you want. If you have more important things to do than to create and manage portfolios, you are better off with a robo-advisor that comes with a portfolio builder that does the job for you. It will work on investing and rebalancing your portfolios against fluctuations of the financial markets. 

Robo-advisors can often do fractional units of mutual funds or fractional shares of ETFs, which allows them to invest very small amounts. Apps like Betterment in the US do not have any account minimum, for instance.

The wealth-as-a-service partners  

If you want to harness the power of robo-advisors for your financial institution and are ready to build your own, we could be the WealthTech partner you need.

At Bambu, we understand the power of technology and how to build it in a way that works for all types of financial institutions. 

Having built white-label robo-advisor solutions for 18 clients (including leading financial institutions like Franklin Templeton, HSBC and Standard Chartered), we are confident of building a robo-advisor solution tailored to your needs. 

Bambu’s founders bring decades of experience in finance and technology. Bambu also has teams in various functions that contribute to building a great robo-advisor: think, UI/UX, AI, R&D and investment, who bring domain knowledge, technical expertise and user-friendly design to all our robo-advisory solutions. As such, Bambu has delivered engaging experiences, and been able to predict financial behaviour and formulate portfolios. 

Speak to us at sales@bambu.co to find out how we can find the right robo-advisory solution fitted for your business and customer’s needs.

Behind every robo-advisor is a team or a set of investment professionals who will provide and guide the investment methodology. After all, 

“Robots capable of manufacturing robots do not exist. That would be the philosopher’s stone, the squaring of the circle.”

―Ernst Junger, “The Glass Bees”

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