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. According to Statista, in 2020, the US had almost $1.05 Billion worth of assets under management in the Robo-advisor segment. 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 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


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 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.


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.


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.


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).

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.


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. 

As the world has evolved, so has our understanding of how financial strategies can help people achieve their goals. Budgeting, investment and responsible borrowing are just some of the strategies we use to grow wealth, and it seems more accessible than ever as digital innovation surges. 

Why, then, are we seeing a widening in the financial advice gap across the globe?

What is the financial advice gap?

Financial advice is any kind of help with the planning of individual life circumstances; preparing for retirement, saving for a rainy day, tax planning, and perhaps the most important of all – how to invest in order to grow wealth.

The ‘advice gap’ then refers to the disparity between people who have access to this financial aid and to those who either can’t afford or access financial advice. 

A survey by OpenMoney UK shows a sharp increase in this gap every year. More people are finding financial advice unaffordable and even more are unaware of available financial advice:

  • The “free advice” gap refers to those who could benefit from advice but are unaware it exists – is estimated to have risen to 20.8 million.
  • The “affordable advice” gap which refers to those who could benefit from advice but can’t afford it – is estimated at 5.3 million.

Financial illiteracy is a huge issue

It’s clear that too many adults aren’t making financially-sound plans or investing for their future.

OpenMoney (2020) reveals that about 44% of adults had run out of money before their next pay at least once in the past year and only 24% of adults save every time they get paid. Whether exacerbated by the COVID-19 disaster or not, it’s abundantly clear that many lack the financial knowledge and capacity to weather another economic crisis.

And it’s not as if financial services are unavailable to the public – numerous financial advisors exist, which can generally be categorised into the following:

  1. Fee-based and commercial-based advisors: individuals or groups typically referred to as “financial planners” who take a fee for financial guidance and management.
  2. Robo Advisors: digital wealth management platforms that automate the process of investing and managing money on your behalf – powered by algorithms, with little to no human supervision.

So why does the financial gap still exist?

When asked about seeking financial advice, many adults revealed that they: 

  • Were simply unaware
  • Many didn’t know of existing financial advisors or where to look.

  • Think it is unaffordable 
  • A recurring consensus was that many financial services were exclusive to the wealthy or simply too expensive.

  • Believe it’s unnecessary
  • They trust their ability to manage their own money and believe they “shouldn’t have to pay for something they can google”. 

  • Distrust conventional advisors
  • Possibly the biggest reason – many believe financial advisors are untrustworthy and only wish to “sell you something” out of self-interest. 

    Digital wealth managers may be the answer

    Robo-advisors are digital, algorithm-driven financial advisors that can help provide adequate, affordable and unbiased financial planning to solve many of issues driving the advice gap:

    • Inexpensive

    The selling point of most robo-advisors is that they require low starting capital (some from as low as $100) and minimums which makes financial advice more accessible and affordable to the general public.

    • Advice is automated

    Based on your risk-appetite, robo-advisors assess your information through a survey to offer advice and tailor a suitable portfolio which automatically invests your money digitally. This means little to no human intervention, removing the element of distrust people may have in conventional advisors.

    • Can be recommended by conventional financial advisors 

    By incorporating both a hands-on approach with robo advisory, financial advisors can offer an even more comprehensive, accessible and personalised financial plan for clients; this could help onboard clients who may previously not have the means for financial services. 

    The potential for Robo-advisors to start closing the financial advice gap isn’t just promising for budding investors. Financial advisors can use this strategy to bring affordable, diversified investing and essential financial advice to an untapped market, creating new opportunities that were previously too expensive and time-consuming to pursue. These opportunities aren’t necessarily short-term, either: financial advisors can reach first-time investors and potentially turn them into long-term clients, capitalising on the scalability of robo-advisors.

    Franklin Templeton, Apex Clearing and Bambu Introduce Tango – A Scalable Goals-Based Wealth Management Tool for Advisors

    New offering brings together three powerful technologies for a personalized, end-to-end solution

    San Mateo, CA, December 10, 2020 – Franklin Templeton today announced the introduction of Tango, a turnkey robo-advisor designed to empower advisors to provide personalized, goals-based wealth management at scale. The all-in-one solution is a collaboration between three industry leaders—Franklin Templeton, Bambu, and Apex Clearing. Franklin Templeton provides the personalized, goals-based portfolio management advice to advisors through its proprietary Goals Optimization Engine (GOE™). Bambu’s white-label platform is the digital solution for clients and advisors, while Apex facilitates trading and custody through its modern back-end platform built for safety, scale and speed.


    “Tango is a single solution built on the expertise of three partners, each providing a unique offering for advisors, and it is truly a case of the whole being greater than the sum of its parts,” said Harshendu Bindal, director of Digital Strategy and Wealth Management for Franklin Templeton. “As the industry continues to move towards digital platforms and technology-based services, investors increasingly expect a seamless digital experience. Tango will give advisors the ability to focus on growing their client relationships with professional management, personalized to their client’s specific goals, without the demands of increased back-office responsibilities or up front charges that are typical of many robo platforms.”


    GOE, a key differentiator in the offering, is Franklin Templeton’s proprietary technology that enables advisors and financial services firms to deliver personalized, high-value services to end-investors at greater scale. Based on proprietary research that won the prestigious Harry Markowitz Award in 2018[i], GOE combines Franklin Templeton Investment Solutions’ portfolio construction expertise with dynamic programming to deliver individualized portfolio pathways based upon an individual’s unique goals. With the ability to handle multiple investor goals, GOE uses probability of success as the driver for the initial asset allocation and each reallocation in order to maximize likelihood of achieving the goal. Portfolio paths further adapt to client changes and market events.


    Apex was one of the first companies to digitize the activities associated with securities clearing and custody to give financial services providers the speed, efficiency and flexibility they need to deliver a better investment experience. Bambu has integrated with Apex’s robust suite of application programming interfaces (APIs) that include new account opening, automated customer account transfer systems (ACAT), funding and trading.


    “From our headquarters in Singapore, we have delivered digital wealth solutions for the most discerning global and US clients. With Tango, we saw an opportunity for a singular offering that makes it much simpler for anyone in wealth management to launch their own white-label robo-advisor. Through a single, bundled relationship, Franklin Templeton, Apex and Bambu have created a turnkey techstack combining proven technology, clearing services and professionally constructed portfolios,” said Ned Phillips, CEO and founder of Bambu.


    “Apex is committed to simplifying investing by leveraging technology to bring relevant and cost-effective investment solutions to individuals and the advisors that serve them. Through this exciting new relationship with Franklin Templeton and Bambu, we’re able to do just that,” said Tricia Rothschild, president of Apex Clearing. “Tango is a powerful solution that brings together a frictionless digital experience with open-architecture technology designed to arm advisors and financial services firms with sophisticated goals-based decision-making capabilities that will improve investors’ relationships with money.”


    Tango is designed for seamless implementation and cost efficiencies that can be deployed quickly and easily into an advisor’s practice. Firms can implement this solution without upfront technology costs in a matter of eight to ten weeks versus several months through competing solutions.


    Franklin Templeton continues to expand its offerings beyond traditional investment products, which now include planning and advice, digital tools, and advisor and retirement platforms. Tango leverages these expanded offerings to weave active investing strategies and advice into a digital platform, enabling financial planners to efficiently run their businesses with custom, optimized portfolios in an open-architecture environment.


    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 approximately 300,000 end users on the platform in 10 countries. Bambu is funded by PEAK6 Investments and Franklin Templeton. Founded in 2016, Bambu is headquartered in Singapore with a subsidiary in the United Kingdom, and United States and EMEA representatives. For more information, visit and follow us on LinkedIn and Twitter.


    About Apex

    Apex Clearing is a custody and clearing engine that’s powering the future of digital wealth management. Our proprietary enterprise-grade technology delivers speed, efficiency, and flexibility to firms ranging from innovative start-ups to blue-chip brands focused on transformation to capture a new generation of investors. We help our clients provide the seamless digital experiences today’s consumers expect with the throughput and scalability needed by fast-growing, high-volume financial services businesses. Founded in 2012, Apex Clearing, a PEAK6 company, is registered with the SEC, a member of FINRA and a participant in SIPC. For more information, visit the Apex Clearing website, and follow the company on Facebook, LinkedIn, and Twitter.


    About Franklin Templeton

    Franklin Resources, Inc. [NYSE:BEN], is a global investment management organization with subsidiaries operating as Franklin Templeton and serving clients in over 165 countries. Franklin Templeton’s mission is to help clients achieve better outcomes through investment management expertise, wealth management and technology solutions. Through its specialist investment managers, the company brings extensive capabilities in equity, fixed income, multi-asset solutions and alternatives. With offices in more than 30 countries and approximately 1,300 investment professionals, the California-based company has over 70 years of investment experience and approximately $1.4 trillion in assets under management as of November 30, 2020. For more information, please visit and follow us on LinkedIn, Twitter and Facebook.



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    Copyright © 2020. Franklin Templeton. All rights reserved.

    [i] The Harry M. Markowitz Award from the Journal of Investment Management and New Frontier Advisors, LLC is an annual award honoring Dr. Harry M. Markowitz, a Nobel laureate in economics, for his legacy and to support future research and innovation in practical asset management. Candidates are taken from among papers published in the Journal of Investment Management each year.