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Top 5 Fraud & Security Posts 2018: AI and Machine Learning

Blog: Enterprise Decision Management Blog

The tremendous interest in AI and machine learning drove the readership on the Fraud & Security blog in 2018. Here are the five posts with the most views.

5 Keys to Using AI and Machine Learning in Fraud Detection

Hand holding analytics

Author TJ Horan, FICO vice president for fraud solutions, wrote a five-part series on the keys to using AI and machine learning in fraud detection. In the first post, TJ discussed the use of supervised and unsupervised models.

Because organized crime schemes are so sophisticated and quick to adapt, defense strategies based on any single, one-size-fits-all analytic technique will produce sub-par results. Each use case should be supported by expertly crafted anomaly detection techniques that are optimal for the problem at hand. As a result, both supervised and unsupervised models play important roles in fraud detection and must be woven into comprehensive, next- generation fraud strategies.

A supervised model, the most common form of machine learning across all disciplines, is a model that is trained on a rich set of properly “tagged” transactions. Each transaction is tagged as either fraud or non-fraud. The models are trained by ingesting massive amounts of tagged transaction details in order to learn patterns that best reflect legitimate behaviors. When developing a supervised model, the amount of clean, relevant training data is directly correlated with model accuracy.

Unsupervised models are designed to spot anomalous behavior in cases where tagged transaction data is relatively thin or non-existent. In these cases, a form of self-learning must be employed to surface patterns in the data that are invisible to other forms of analytics.

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Fraud Detection: Applying Behavioral Analytics

In this post, TJ Horan described the kinds of behavioral analytics used to understand and anticipate behaviors. A critical part of this, he noted, is behavioral profiles:

Given the sophistication and speed of organized fraud rings, behavioral profiles must be updated with each transaction. This is a key component of helping financial institutions anticipate individual behaviors and execute fraud detection strategies, at scale, which distinguish both legitimate and illicit behavior changes. A sample of specific profile categories that are critical for effective fraud detection includes:

Chart with seven types of behavioral analytics

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Fraud Detection: Adaptive Analytics and Self-Learning AI

In the third of his posts to crack the top five for last year, TJ Horan explained how adaptive analytics improve sensitivity to shifting fraud patterns.

Adaptive analytics technologies automatically adapt to recent confirmed case disposition, resulting in a more precise separation between frauds and non-frauds. When an analyst investigates a transaction, the outcome — whether the transaction is confirmed as legitimate or fraudulent — is fed back into the system to accurately reflect the fraud environment that analysts are facing, including new tactics and subtle fraud patterns that have been dormant for some time. This adaptive modeling technique automatically modifies the weights of predictive features within the underlying fraud models. It is a powerful tool that improves fraud detection performance on the margins and stops new types of fraud attacks.

Adaptive analytics diagram
Source: FICO Blog


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Preventing Application Fraud with Machine Learning and AI

FICO 25 years of AI and machine learning logo

In a preview of her FICO World presentation, Liz Lasher discussed how adaptive analytics, supervised and unsupervised models, and other AI and machine learning techniques are being applied to catch application fraud. In this realm, she argued, explainable AI is critical:

Many machine learning algorithms are considered “black box” models that do not give fraud analysts, consumers, or regulators the appropriate insights into decisioning logic, e.g., “Why am I being declined for credit?”

This is why explainable artificial intelligence is so important: to impart the necessary transparency to pass regulatory muster, while maintaining accuracy of prediction.

FICO are very cognizant of the impact of regulations to our business and for our clients. In fact, we pride ourselves in leveraging mathematical innovation to solve problems in the real world. In the area of account originations, our credit risk and fraud scores are designed to be a tool to assist lenders with compliance to applicable fair lending laws such as the Fair Credit Reporting Act, Regulation B, and the Equal Credit Opportunity Act (ECOA).

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How Fraud Changes in the World of Real-Time Payments

Diagram of different fraud points

Real-time payments are opening up new avenues for fraud, wrote Sarah Rutherford. The speed of these payments makes it more difficult to trace the proceeds of crime, while also making it easier for criminals to move and extract funds. She noted three areas of concern as examples:

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Follow this blog for our 2019 insights into fraud, financial crime, cybersecurity and AI and machine learning.

The post Top 5 Fraud & Security Posts 2018: AI and Machine Learning appeared first on FICO.

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