Banking

AI and Machine Learning in Banking

Banks are facing challenges from all sides, including emerging threats from new technology-enabled Fintech competitors, stricter regulatory requirements, and pressure to simplify the client experience while simultaneously reducing costs. Implementing AI and machine learning in banking capitalizes on a once-in-a-generation opportunity for your bank to expand market share, deepen customer relationships, and compete for and win new business — all while efficiently complying with regulations and fighting financial crime.

See how banks are winning with AI.

Download Overview

AI and Banking

Both companies and consumers expect their banks to understand who they are, anticipate their needs, and be ready with relevant financial solutions. Banks need to deliver these solutions seamlessly across multiple channels, offering convenient access from any location, on any device. To stay competitive, you must nurture existing relationships while finding new clients in new markets. You must also compete aggressively to find the best businesses, rather than waiting for businesses to find you. AI and the application of machine learning in banking have the power to address these goals by leveraging data from your existing clients — including how their financial needs have evolved and their channel preferences changed.

Client Experience

Determine which client is likely to need specific products or services
Deepen your relationships with customers
Anticipate client needs and identify new needs as they arise
Precisely target offers
Ensure clients get the support they need when they need it
Use analytics to understand client price sensitivity and preferences

Lending

Build more precise credit models
Find and compete for the business with the best risk-adjusted return
Actively manage your client portfolio
Be a leader in small business credit with superior analytics
Proactively intervene when clients experience financial stress
Forecast losses more accurately

Financial Markets

Reduce middle and back office costs related to process failures and error corrections
Improve pricing and capture the best business opportunities
Optimize trade execution and routing
Match investment opportunities to potential investors
Get research reports to the right clients

Client Experience
- Determine which client is likely to need specific products or services
- Deepen your relationships with customers
- Anticipate client needs and identify new needs as they arise
- Precisely target offers
- Ensure clients get the support they need when they need it
- Use analytics to understand client price sensitivity and preferences
Lending
- Build more precise credit models
- Find and compete for the business with the best risk-adjusted return
- Actively manage your client portfolio
- Be a leader in small business credit with superior analytics
- Proactively intervene when clients experience financial stress
- Forecast losses more accurately
Financial Markets
- Reduce middle and back office costs related to process failures and error corrections
- Improve pricing and capture the best business opportunities
- Optimize trade execution and routing
- Match investment opportunities to potential investors
- Get research reports to the right clients

High Value Use Cases in Banking

There are hundreds of enterprise AI applications in every function and business line in a bank. By using AI in consumer, investment, and commercial banking, your bank — whether large or small — can drive revenue growth, differentiate your brand by offering a superior client experience, reduce operational costs while improving quality, and improve risk management effectiveness and efficiency.

Check out all Banking use cases

Credit

In the world of credit, the best models win. Banks are using AI to build better models for estimating default probability and loss severity, and for loss forecasting. These models help improve pricing for risk, credit approval, and portfolio management. Building more granular models with enterprise AI also makes credit scoring more precise, as models learn the nuances of discrete populations.
Financial Crime

As criminals get more and more creative with their tactics, banks face increased pressure to stay ahead of bad actors when fighting financial crime, especially money laundering and fraud. Using AI, banks are learning new insights from their investigational findings and fraud losses and training models to accurately detect suspicious activity or to spot and prevent fraud in real time. And these models continue to get better over time as they learn new types of malicious activity.
Client Experience

Clients expect banks to know who they are, what they need, and when they need it. Drawing from data on clients in similar situations, banks are using our enterprise AI platform to predict client needs. Some banks are identifying event triggers which may indicate that a new need has arisen. Reviewing client complaints, for example, can indicate where your bank’s attrition risk is highest and prompt you to take action. These insights also help you to build predictors of traffic volume (in branch, in contact centers) so that you can staff each unit appropriately.
Marketing

Banks are using our enterprise AI platform to predict which prospects are likely to become the most profitable clients and are using this ability to prioritize leads and referrals. Banks are learning from clients to target their offers more precisely, an imperative with digital advertising. Your bank can use sophisticated analytics to predict client price sensitivity, tailor your value proposition, and estimate price-volume elasticity.
Cash Management

To improve cash management, banks are using enterprise AI to predict new loan demand, to anticipate prepayment speed, and forecast ATM cash requirements. Banks are using historical data on cash inflows and outflows to build models to predict cash availability. These insights enable your bank to have the right amount of cash on hand where and when you need it and to optimize your return on excess cash.
Global Markets

In financial markets, traders are using historical transaction cost analysis (TCA) and execution data to build models that optimize order routing and trade execution strategy. These models evaluate the relative merits of the numerous potential algorithmic trading approaches, venues, and counterparties. These support trader decision-making and help to minimize market impact and cost while demonstrating and recording your efforts to fulfill execution requirements.

DataRobot Helps Banks With:

Chief Data Officers

Increase the productivity of your data science team.
With enterprise AI, you can get the productivity of a large data science team from a small one. Let DataRobot find the best models for you and use DataRobot’s simple deployment options to get them to market faster. Relieve data scientists from documentary requirements by using DataRobot’s automated model risk management and model validation templates.
Business and Function Heads

Leverage AI and machine learning even if you do not have deep data science talent.
Tap into the deep expertise in your data that your bank already has. Enable business analysts and data analysts without formal data science training to build and use sophisticated models.
Chief Technology Officers

Bring AI and machine learning-based solutions to market faster.
Get models to production faster using DataRobot’s low-risk model deployment options, including code generation, deployment to Spark, and API-based deployment capability.
Chief Data Scientists

Annihilate your backlog of analytics requests.
Let DataRobot suggest the best model in each situation, saving you the time and effort of trying and comparing every model. Use AI to build many models at the same time it takes to build one, increasing precision with more model granularity. Let DataRobot handle the low-risk models from start to finish so you can focus your talent where the payoff (or the risk) is the greatest.
Chief Information Officers

Monetize your investments in data infrastructure.
The bottleneck in many banks is no longer a lack of data. In fact, there’s plenty of data but not enough analytics staff to convert that data into actionable insights. Democratize data science with DataRobot and watch the performance of your business take off as the data reveals opportunities and improvements.

We use DataRobot to predict everything from who is likely to pay to who is likely to respond as well as fraud scenarios.

Deena Narayanaswamy
Avant Head of Data Insights
I’ve never had so much ease explaining the inner workings of my models as I do with DataRobot.

Akshay Tandon
VP of Strategy Analytics, LendingTree
DataRobot's platform allows users to build and deploy highly accurate machine learning models in a fraction of the time it takes using traditional data science methods.

Loretta Ibanez
Freddie Mac Director, Mortgage Innovation