AI and Banking
As an ever increasing number of fintech companies make an already competitive market even more so, banks are being forced to look for ways to improve the effectiveness and efficiency of their business. AI helps banks improve their bottom line with the people they already have and the data they’re already collecting.
- Optimize customer selection
- Deepen relationships with customers
- Better targeting of new customers
- New products and services powered by AI
- New business models for existing products and services
- Increase customer satisfaction
- Optimize inefficient loan approval processes
- Send market research only to interested investors
- Optimize call center operations
- Build and deploy models cheaper and faster
- Reduce unnecessary AML investigations
- Upgrade Know Your Customer (KYC) programs
- Forecast losses more accurately
- Enhance scenario and stress testing
- Streamline model risk management
- Better detect and prevent fraud
- Enhance cybersecurity detection and prevention
Banking Use Cases
Banks are using machine learning to increase top and bottom line through gaining competitive advantages, reducing expenses, and improving efficiencies. They are optimizing all areas of their business from risk analysis and fraud detection to marketing, in order to make data-driven decisions that lead to increased profitability.
AI helps risk teams better manage exposure to many types of risk – from credit risk and market risk to model risk and regulatory risk. With applications ranging from anti-money laundering (AML) and Know Your Customer (KYC) to fraud detection and prevention to model risk management and credit risk forecasting, DataRobot helps Chief Risk Officers and their risk teams maximize their resources and deliver better risk protection.
Wholesale and commercial bankers are able to better target clients and more efficiently and profitably manage their business with AI. DataRobot has banking clients of all sizes optimizing use cases ranging from sales and prospecting to credit risk and pricing to automated loan approvals and early warning systems.
Treasury and Cash Management
With AI, bankers are able to provide better predictions to their client’s corporate treasurers, allowing them to more efficiently manage their liquidity. Using DataRobot, bankers provide smart accounts receivable data, improved not sufficient funds (NSF) alerts, and detailed cash flow forecasting that allows their clients to better manage their accounts.
From better targeting to customer retention to operations, AI helps retail banks stand out from competitors. With a wide range of applications – from more targeted marketing and call center optimization to churn risk and credit risk modeling – DataRobot helps retail banks stay competitive in an increasingly crowded marketplace.
Special Assets / Workout
Special assets teams can use AI to determine the most efficient, highest value approaches to managing delinquent loans. Whether it’s prioritizing and allocating resources optimally, delivering better loan valuations for recoverability and cost models, or valuing portfolios for securitization and secondary markets, DataRobot offers the tools and speed for your special assets teams.
AI helps merchant services organizations win and keep the best clients. By providing better credit and merchant fraud detection, identifying prospects for targeted marketing efforts, or deepening relationships with the right premium products and banking services, DataRobot helps merchant services providers offer the services their customers need.
From customer selection to operations and marketing, AI can touch every part of the highly competitive card business. DataRobot helps improve credit card service and support, including customer selection and retention, call center optimization, and rewards targeting and marketing, as well as providing better application and transactional fraud detection and collections optimization.
From managing interest rate and prepayment risk to improving collections, AI helps mortgage companies and services operate more efficiently and profitably. Applications where DataRobot excels include predicting prepayment speeds and refinancing likelihood, as well as early warning detection for delinquencies and prioritizing collections to make the best use of your outbound callers’ time.
AI is a big opportunity for investment banks to target the right companies and to optimize their services. Whether it’s acquiring new customers or matching up investors with opportunities, tools like DataRobot help investment banks to operate more efficiently and drive higher profits.
Trading & Markets
The trading business is notoriously complex from an operations perspective. From trade settlement to order routing, making the plumbing more efficient means big savings. With DataRobot, middle and front office teams also benefit from research recommendations, bid/ask spread optimization, execution strategy, and many more AI applications.
Asset Management & Alternatives
Operational AI use cases like smart order routing or predicting settlement issues increase the efficiency of trading, and automated research analysis and aggregation and make investors smarter and better at their jobs. DataRobot also drives big impacts to profitability by helping asset managers predict M&A propensity or rating/outlook changes.
Customer retention represents a substantial challenge for most wealth managers, but by using AI, wealth managers can identify the best new clients and provide early warning about potential customer attrition. DataRobot helps wealth managers with use cases ranging from new customer prospecting to customer attrition to relationship deepening. DataRobot also offers the tools for wealth manager to developer their own robo-advisor offerings.
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 significant cost and time to model deployment.
Our competitive advantage in machine learning automation accelerates the efficiency of the 1st and 2nd lines-of-defense by automating time-consuming compliance processes required by regulation. A standardized approach to model building and evaluation, including automated compliance documentation and challenger models means better, safer models in less time.