This episode of More Intelligent Tomorrow brings together H.P. Bunaes, the Executive Director for AI. & Machine Learning at JPMorgan Chase and Diego Oppenheimer, the Executive Vice President of Machine Learning Ops at DataRobot for a conversation about artificial intelligence, machine learning, and their roles in the financial services industry.
Data analytics and AI/ML wasn’t a thing in 1983 when H.P. Bunaes started his career. But since then, it’s become a core element for businesses who want to succeed in the new economy.
It’s never been a better time to be in data and analytics than it is right now.”
With that being the case, Diego wonders why banks, who have so much data, still struggle with implementing AI/ML.
H.P. believes it’s because executive management delegates the task to technical teams and hopes for the best. Successful implementation of AI/ML requires leadership from the very top.
It also requires selecting the right project to apply AI/ML. The temptation is to start with big, high visibility projects. But you have to walk before you can run. Starting with overly complex projects will lead to disappointment and wasted resources. Don’t just pick what’s shiny and new. Come at AI/ML from a business perspective and select a project where there’s potential for attainable and measurable success.
Another crucial decision is to have the right people working on your projects. There are few data scientists who understand banking, and you need someone who not only knows the data, but also your business needs.
H.P. suggests imagining the press release for your project and working backward from there. Know the end goal and build your solution to reach it. Too often there’s a tendency to look at the data available and see what you can learn from it. Instead, figure out what you need and build your AI/ML solution to meet that need.
Turning specifically to the world of banking and financial services, Diego asks H.P. where he sees opportunity.
Credit. It’s where analytics began in the world of financial services, and it remains a deep well of opportunity. From predicting default to more advanced areas like collateral valuation, severity loss, forecasting, and reserve analysis, there are a lot of possibilities. Using AI/ML to differentiate risk and go deeper into the credit spectrum is a terrific way to separate yourself from your competitors.
AI/ML should be used to manage risk rather than to minimize it. If you design your control infrastructure for the highest risk, you’ll find you can’t move. Make a control infrastructure that’s sensitive to the data, and you’ll be more agile.
You can’t be in business and not take risks.”
Much of the financial services industry resists moving to modern AI/ML solutions. The cloud is still an unknown for a lot of executive leadership, and there’s an inertial bias toward proven legacy policies and procedures. Moving to the cloud means having to rethink those processes.
For a company to survive, moving to the cloud is inevitable. With access to unlimited computing power, endless storage, and the latest machine learning models in the cloud, the legacy model of an on-site data center is becoming obsolete.
If you’re ready to make the move to AI/ML, H.P. shares some important considerations.
AI/ML isn’t like any other piece of software. It doesn’t fit the normal software development lifecycle (SDLC). It needs a different kind of nurturing. It involves experimentation. Failure is part of the process.
If you create control processes which are too inflexible for AI/ML, you’ll find people will sidestep those processes and set up a skunkworks outside your control. What you need is a development lifecycle that meets the unique needs of AI/ML and is a framework people can follow.
The companies who do AI/ML right will separate themselves from the rest of the pack and be more competitive. You must have the right talent, the right support, and the right goals, all implemented to work with as little friction as possible.
Get educated. Know what AI/ML can achieve. Be able to spot an opportunity and understand what your analytics teams are working on and why.
Organizations that take a “wait and see” approach to AI/ML may find themselves left behind and in an unrecoverable position.
In this episode, Diego and H.P. share their thoughts on:
- Using risk management as a competitive advantage
- Building out an ML framework
- Understanding ML governance
- Bringing IT and analytics teams together
- Moving faster with the help of executive guidance