Predict ATM Fraud

Financial Services Risk / Security Decrease Costs Improve Customer Experience Reduce Risk Executive Summary Fraud Detection
Predict ATM fraud in order to take preventative action.
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Overview

Business Problem

The number of compromised ATMs in the U.S. skyrocketed 546% from 2014 to 2015 (FICO). Now, ATM fraud is growing at an alarming rate in the US and non-bank owned ATMs are the most vulnerable to this abuse. In order to ensure that ATM’s are able to provide consumers with optimal cash management services, it is vital to predict potential instances of ATM fraud so financial institutions are able to protect themselves and their clients adequately. ATM fraud is one of the fastest-growing electronic crimes committed against banking institutions, card skimming fraud alone results in billions of dollars in annual losses annually. Due to the increased rates and methods used to commit ATM fraud, financial institutions are actively searching for enhanced security protocols to protect their clients and their businesses.

Intelligent Solution

AI will empower your organization to make predictions of fraud based on historical customer transaction data. By leveraging AI your organization is able reduce ATM fraud, in turn improving customer satisfaction and reducing liability costs. AI will give your organization insights on where and when the probability of fraud is highest. These insights allow preventative actions, like card freezes, to be taken when a transaction poses a high risk of fraud. Before approving risky transactions, AI will allow you the opportunity to communicate the transaction attempt with the account holder in order to validate the transaction. While rules based systems are useful for compliance requirements, they often result in higher amounts of false positives, which are transactions that are clean but classified as fraudulent. AI models will supplement your rules based systems and reduce the total amount of false positives, ultimately streamlining your customer experience without hindering your efforts to reduce fraud. In addition, anomaly detection will help you identify cases of new fraudulent behavior that may not be captured by your existing rules based systems.

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