How to improve bank fraud detection with data analytics
Financial institutions need comprehensive analytics to build a strong bank fraud detection strategy. Advanced analytics software provides the tools necessary for banks to recognize and act on suspicious patterns, quickly notify customers of fraud incidents and position themselves for faster settlements.
Pattern analysis helps detect fraud
Analytics can dive deep into data and look for patterns that indicate potential fraud. For example:
- Customers with deposit, checking, credit card and personal loan accounts have usage patterns that deep analytics can combine and check against its own fraud indicators. For instance, a bank's fraud prevention system can be set up to trigger a temporary hold on unusually high transactions until the charges are confirmed with the account holder.
- Information Age reports that pattern analysis of average balances, number of bounced checks and other customer attributes can help banks detect potential check fraud.
- Bank fraud detection indicators for new accounts might include application anomalies, unusually high purchases of popular items or multiple accounts being opened in a short period with similar data, according to Equifax.
Fraudsters tend to have telltale attack patterns, as do the events themselves. For example, fraudsters often tie scams to seasonal events; tax-related scams are common during tax season. Banks can be liable if the schemes include bank wire transfers on behalf of customers, according to American Banker, so it is in their best interest to track and anticipate these attacks using predictive analytics.
Other times, fraudsters may launch several similar attacks or work in concert with other criminals. Analytics can show relationships among fraudulent activities, including several suspicious activities in a single account or patterns of similar activities across different accounts, according to Computer Business Review. Fraudsters often test for bank limits, so they will attempt to conduct a series of transactions that are just below those limits, either by themselves or in concert with others.
Deep analytics looks for commonalities that could indicate fraud among transactions or sets of transactions. The relationships are increasingly complex, so they often evade simple monitoring techniques. In fact, IDT911 estimates that 85 percent of identity theft goes unnoticed by traditional monitoring tools.
Data triggers fast resolutions
Quick fraud detection is essential to minimizing losses. The faster a bank detects fraud, the faster it can restrict account activity. Jose Diaz, director of payment strategy at Thales e-Security, explained in a recent interview with IBM that this strategy can minimize losses for both the financial institution and its customers.
For instance, IDT911 reports that quicker detection and notification of fraud provides credit unions with an enhanced reputation while saving money for members. Fraud detection within the first day costs consumers about $34, compared to $1,061 per claim if the fraud isn't noticed for three to five months. The source noted that electronic monitoring and analytics speed up detection time by as much as 18 days compared to paper methods.
Fast fraud detection and remediation is important for maintaining customer relationships as well. Customers are extremely sensitive about the security of their financial information. The Financial Brand cited three separate studies where people said they would rather have naked pictures of themselves posted on the Internet than have their financial information compromised. This extreme example demonstrates just how important it is for banks to keep their customers' information secure. If they fail to do so, it could cost them the business of valued customers.
Today, people expect more from their banks, including faster fraud detection and seamless resolutions. Banks that want to meet these customer demands and position themselves ahead of competitors should embrace the benefits that banking analytics can provide in the fight against fraudsters. Learn more about leveraging analytics to detect and mitigate fraud and financial crime with IBM Counter Fraud Management in a demo video.