DataRobot in the News
Global Banking and Finance Review: Strengthen Anti-Money Laundering with Machine Learning
Banking Technology: Fintech Connect Live 2017 – The Unleashing of AI and ML
PRMIA Intelligent Risk: Using Automated Machine Learning to Minimize Model Risk
Our expert says:
“DataRobot empowers business experts to do technical analysis without advanced degrees. The people who know the problems the best are now the people who are best equipped to solve them. Automated machine learning is the key to unlocking the potential of your business teams.” - Greg Michaelson, DataRobot’s General Manager of Banking
Commercial banks are facing disruption from emerging fintech companies. Traditional legacy banking practices aren’t sufficient to keep up with this new competition. The commercial banking industry must embrace artificial intelligence to stay ahead of the game, but traditional tools are slow and expensive, and hiring the people who know how to use them is next to impossible. DataRobot enables bankers, relationship managers, and analysts who already know the business to perform these analyses independently. Using automated machine learning to predict prepayments, avoid credit risks, prospect new customers more efficiently, and deepen relationships with existing customers will make a significant impact on the bottom line for commercial banks.
Consumer banks collect and have access to mountains of potentially useful data, but most regional banks and credit unions don’t have the data science resources to take advantage of this valuable information. DataRobot’s automated machine learning platform helps data science teams multiply their productivity and turns business analysts into data scientists. That means faster speed-to-market and better solutions to core banking issues. Fraud detection, measuring credit risk, pricing, and accurate anti-money laundering systems can make all the difference for consumer banks.
For the regulatory bodies overseeing the biggest banks in the world, evaluating models is a crucial part of the process of ensuring systemic stability and capital adequacy. DataRobot’s systematic, automated approach to modeling can automatically generate benchmark models and perform adversarial model comparisons in minutes. This means more thorough examinations in less time.
Model Risk Management
Internal model risk management teams are under intense pressure to deliver independent evaluations of predictive models quickly and accurately. DataRobot’s systematic approach to automatic model tuning; data partitioning; and evaluating a large number of diverse, independent modeling approaches provides confidence that model validation efforts are thorough, rapid, and effective. Learn more about model risk management.
The opportunity to use data science in markets is huge. Buy-side data science teams in markets can use DataRobot as a rapid prototyping tool to backtest and evaluate thousands of potential trading strategies. Sell-side companies can use DataRobot to predict customer trading behavior, identify the perfect audience for a research report, rank investors by potential interest in an IPO, and more.
DataRobot Helps Banks With:
Anti Money Laundering
Machine learning will reduce false positive rates and increase accuracy, leading to greater efficiency, lower costs, and reduced exposure to regulatory fines and issues.
Identifying the highest quality business and commercial banking customers with limited data is critical to success, but most banks take a subjective and inconsistent approach. Machine learning will improve close rates and increase revenues using data that banks already collect.
Banks lose billions of dollars to fraud every year. Real-time machine learning solutions will evaluate transactions in real time and prevent losses before they occur. DataRobot’s automated machine learning platform enables banks to build their own anti-fraud solution, reducing their reliance on black-box vendor models.
Model Validation and Risk Management
Banks want to build, deploy, and use predictive modeling to improve the bottom line, but regulation and sound risk management represent a significant cost and friction to speed-to-market. A systematic approach to model building and evaluation, including automated challenger models, means better, safer models in less time. Learn more about model validation.
Credit and Prepayment Risk
Credit risk models are the bread and butter of the banking industry, but in many cases development and use of these models have not penetrated the commercial banking space. Automated machine learning enables business experts in commercial lending to use the same tools that their consumer counterparts are using.
When the network goes down, business stops. Banks capture terabytes of logs every day that contain vital information about the health and security of banking infrastructure, but this data is rarely used to predict potential problems. DataRobot enables banks to monitor their systems 24 hours a day.
What our Customers Say:
"“At LendingTree, we recognize that data is at the core of our business strategy to deliver an exceptional, personalized customer experience. DataRobot transforms the economics of extracting value from this resource.”"Akshay Tandon
VP of Strategy and Analytics, LendingTree
"“DataRobot allows us to understand the data that’s being fed into our models without blindly feeding whatever we get into our system. DataRobot makes my team very effective.” "Deena Narayanaswamy
Head of Data Insights, Avant