AI-Driven Fraud Prevention
12 Considerations for evaluating AI platforms focused on production-ready outcomes, democratization, and explainability.
Avoid human bias in lending opportunities and access to capital with this proven framework and set of ML definitions
Use this framework to help your market participants, regulators, policymakers, and other stakeholders speak the same ML language.
AI for Payments Fraud
Enhance customer experiences by preventing fraud and minimizing legitimate transactions from being blocked as fraud.
- Fraud Prevention
- ML
- Payments Fraud
Customer Results
2X
Doubled propensity to buy new bank products by decreasing credit risk scoring time
10X
Astounding timesaver for our team by achieving faster real-time fraud scoring
6X
Improved customer experience and credit operations with faster model development
"Driverless AI is giving amazing results in terms of feature and model performance..we experienced a 6X speed up when using H2O4GPU with Driverless AI."
- Venkatesh Ramanathan, Senior Data Scientist PayPal
"With Driverless AI, new model development time for us has gone down from several months to just over a few days now."
- Vinay Pai, SVP Engineering, Bill.com
Stopping Financial Fraud in its Tracks with AI
Fraud detection is now an AI domain, and algorithms spanning classical machine learning to neural networks are being leveraged to fight fraud. AI can be used to analyze large volumes of transactions to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to reject transactions outright or flag transactions. The AI model can also provide reason codes for the decision to flag the transaction. In this virtual meetup, we will go over Fraud Detection in the finance industry and look at a live demonstration of AI solutions with real-world datasets.
Forrester Research: Artificial Intelligence Is Transforming Fraud Management
Improve risk scoring, predictive case investigation, and contextual reporting.
Read this Forrester report, in which it identifies key fraud management use cases where AI can help and maps how security and risk (S&R) professionals can use major AI technologies in each situation.
Use Case
Fraud Detection
AI can be used to analyze large volumes of transactions to find fraud patterns and then use those patterns to identify fraud as it happens in real-time. When fraud is suspected, AI models can be used to reject transactions outright or flag transactions for investigation and can even score the likelihood of fraud, so investigators can prioritize their work on the most promising cases.
The AI model can also provide reason codes for the decision to flag the transaction. These reason codes tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to fraudulent activities.
Use Case
Lending Risk
AI is a great solution for credit scoring using more data to provide an individualized credit score based on factors including current income, employment opportunity, recent credit history, and ability to earn in addition to older credit history.
This more granular and individualized approach allows banks and credit card companies the ability to more accurately assess each borrower and allows them to provide credit to people who would have been denied under the scorecard system including people with income potential such as new college graduates or temporary foreign nationals.
AI can also adapt to new problems, like credit card churners, who might have a high credit score, but are not likely to be profitable for the card issuer. AI can also satisfy regulatory requirements to provide reason codes for credit decisions that explain the key factors in credit decisions.
Use Case
Money Laundering
AI, especially time series modeling, is particularly good at looking at series of complex transactions and finding anomalies. Anti-money laundering using machine learning techniques can find suspicious transactions and networks of transactions. These transactions are flagged for investigation and can be scored as high, medium or low priority so that the investigator can prioritize their efforts.
The AI can also provide reason codes for the decision to flag the transaction. These reason code tell the investigator where they might look to uncover the issues and help to streamline the investigative process. AI can also learn from the investigators as they review and clear suspicious transactions and automatically reinforce the AI model’s understanding to avoid patterns that don’t lead to laundered money.