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How can DataOps improve your financial institution’s fraud program and mitigate risks?
Learn more about the role of DataOps in fraud programs and risk mitigation.
Fraud comes in different forms, from client-facing credit card fraud to internal fraudsters twisting the loan portfolio. Banks (and other financial institutions) need to stay vigilant and act fast to prevent the loss of both money and reputation that follows each fraudulent incident.
How efficient is your fraud program?
Fraud is expensive, but fraud prevention, detection and remediation can also be costly. The cost-benefit analysis of fraud programs is tricky, but if you see your financial institution falling short on any of the following issues, it’s time to re-evaluate your fraud program:
Rising fraud cases and losses
Increase in customer fraud complaints
Increased fraud-tackling costs
Delayed fraud claim investigations and resolutions
No formalized accountability or responses to fraud allegations (aka, ad hoc responses)
Increased backlog of fraud alerts awaiting investigation
The four stages of fraud response
Each bank goes through four stages when responding to alleged fraud:
Banks must take an honest look at the four stages and identify potential areas for improvement in their current processes and procedures. Any gaps can be filled with more personnel, improved processes or specialized tools, such as automated fraud detection systems.
Four main advantages of an automated fraud detection system
There are several advantages to fully automated fraud detection systems:
Quicker detection. The sooner a fraudulent behavior is identified, the faster a bank can act to prevent it… and as speed of response is directly associated with cost, time is of the essence. Unlike humans, automated systems shift through data faster and can work around the clock without the need for sleep, thus increasing the speed of fraud detection. Real-time or near real-time responses help to identify fraud as soon as it occurs and nip it in the bud.
Constant improvement. The detection abilities of automated systems can be assessed by their accuracy. This single metric of system performance offers both a baseline and a target for improvement. Machine learning experts, statisticians and analysts can work against this objective goal to constantly improve the performance of the fraud detection algorithms.
Traceability. The third stage of fraud detection is investigations, in which domain experts must shift through heaps of data to determine the source of potential fraud. Automated systems can trace data lineage more efficiently, especially when they are architectured for traceability. This shortens the investigation cycle, provides clarity to fraud programs and speeds up fraud response times.
Patterns. Automated systems work well with patterns. Identifying several different fraud trends informs the bank of the types of fraud taking place, as well as helping to develop deterrent and preventive policies in response to the main patterns.
DataOps tools and practices can be utilized to achieve more than just fraud detection.
When financial institutions are satisfied with their level of fraud detection accuracy, DataOps platforms can be engineered to provide remediation features. For instance, a fraud detection system with a 95% accuracy can trigger the automatic freezing of transactions for the flagged account until human investigators resolve the fraud allegations.
Even beyond fraud, DataOps infrastructure is well suited to automate other processes involving risk. As an example, banks find it beneficial to automate stress testing. They test their current risk capacity (earnings, capital and regulatory requirements) against automatically simulated risks:
The DataOps platform can automatically take the risk capacity and run models against simulated risk levels to generate stress-testing information on a regular basis. Banks can then use this to assess the adequacy of their minimal capital, solvency, expected earnings, regulatory leverage and liquidity in different risk scenarios.
The advantages of automating risk assessment are the same as those of automated fraud detection. DataOps gives financial institutions faster responses, constant model improvement and traceability to determine how the current state can be improved.