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DataRobot’s AI Cloud for Financial Services Unlocks the Art of the Possible
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DataRobot’s AI Cloud for Financial Services Unlocks the Art of the Possible

As management teams at financial institutions look for best practices to make part of their regular toolkit, they are reaching most for the ones that increase the speed and reduce the risk of large-scale change.

That forward-thinking approach can lead financial institutions to leverage AI technology, which can help give decision-makers trusted tools to solve integral challenges vital to the health of the business. One of the leading providers of AI and machine-learning software, DataRobot continues to attract clients in financial services who want to de-risk their AI investments and rapidly scale AI to almost every part of their operations, resulting in improved productivity and higher customer satisfaction.

Based in Boston and operating since 2012, DataRobot allows its clients to systematically create, deploy, manage, and govern AI at global scale. Work that used to take weeks now transpires within hours, allowing employees to focus on their immediate tasks while letting DataRobot manage areas such as compliance automation, fraud detection, and dynamic pricing of assets.

Financial institutions are increasingly seeing AI technology as a competitive advantage, said Jay Schuren, chief customer officer at DataRobot. “AI empowers the financial services industry because it not only helps them save money and mitigate fraud but also improves job satisfaction and the rigor of documentation.”

A McKinsey report argues that linking AI and banking is a savvy strategic decision that benefits the bottom line: “The potential for value creation is one of the largest across industries, as AI can potentially unlock $1 trillion of incremental value for banks, annually.”

The interest in ushering AI into business sectors isn’t abating any time soon either. Global spending on AI is forecast to double over the next four years, growing from $50 billion in 2020 to more than $110 billion in 2024, as an OECD report found.

What AI does best is parse through Big Data to speed up laborious processes and uncover patterns that the human eye can’t track easily. That same report cited above goes on to say how, for the investment community, “information has always been key and data has been the cornerstone of many investment strategies, from fundamental analysis to systematic trading and quantitative strategies alike.”

In 2021, worldwide banking and securities industry IT spend was more than $200 billion, Schuren said. “DataRobot believes every penny of that should deliver value. DataRobot does this by providing decision-makers across the financial services industry with trusted AI solutions to solve mission-critical problems.”

DataRobot can handle a variety of tasks, Schuren said, such as setting prices in advance for assets, whether those are bonds or currency exchanges. The unique aspect of the DataRobot AI Cloud platform is the granularity of answers you can get, and the speed at which you can get those answers. “For example, how do you determine customer churn? Instead of answering that question for a subset of clients within a set timeframe, you can take a look at every single customer and see what their propensity to churn is in a month or two or three months. You can ask the question by geography, time frame, or any other characteristics. DataRobot will automatically try various models across all of those different characteristics at scale, and then we can find the ones that work and can cast a wide net. Instead of spending a month to ask one question, we can ask several hundred and determine the ones that are really robust, and it’s about getting high-quality answers as fast as possible.”

For areas such as lending, DataRobot automated the process to eliminate bias in models. Since there are many ways to determine fairness, it provides an outline that helps organizations align values that matter to them, all centered on predictive analytics. Schuren said, “When it comes to algorithms and models, you can ask it questions on where it’s biased, and when you think about getting rid of bias within lending strategies, it’s done much easier with DataRobot.”

Addressing fraud continues to be top of mind for financial institutions: Banks faced more monthly fraud attacks in 2021 than the year prior, according to a study from LexisNexis Risk Solutions. The study also found the average volume of monthly fraud attacks for banks earning more than $10 million in annual revenue has increased since 2020 from 1,977 to 2,320.

Schuren explained that most fraud systems are driven by rule-based systems that evolve over time and could become as simple as identifying if a transaction is above a certain threshold. “Those rules can number in the thousands, and we’ve implemented a workflow that can be very useful where, within that process, if the transaction is safe, it goes right through, or a transaction can be flagged for review thanks to adding different machine-learning models along the way.”

The fraud team at a bank doesn’t have to invest extensive time and resources when an AI system such as DataRobot can train the models to reduce false positives. It all comes back to efficiency and saving time in the right places.

A real-life example showcases the technology in action. When Valley Bank’s anti-money laundering team sought to reduce the manual work involved in predictive modeling related to money laundering, it used DataRobot AI Cloud to optimize the AI life cycle. The result was decreasing total alert volume by 22% and increasing escalation to case by three percentage points.

“From ingesting the data to performing data quality to developing and testing models to deploying them … the platform does everything for us with minimal manual intervention. I haven’t found another tool that does that,” Chris Mendoza, director of financial crimes technology at Valley Bank, said in a statement.

Banks need to secure a competitive advantage in an increasingly tight race to harness best-in-breed technology. Decision makers need to not just plan a future-ready strategy, but also recognize the value of AI that could boost not just their performance in-house but also their reputation among their global customers.