Utilizing machine learning to secure financial networks
Leveraging real-time and stored data to mitigate fraud and protect customers.
According to recent research, 2016 was critical year for security in the financial arena as identity fraud reached new heights. The report from Javelin indicates that “the overall fraud incidence rose 16 percent to affect 6.15 percent of U.S. consumers, from 5.30 percent in 2015 — the highest on record.”
This growing prevalence of fraud has proved challenging for financial institutions to sustain strong, and long-lasting, customer relationships. Trust is waning. Financial institutions must implement strong security procedures to protect their users and provide data loss prevention measures. Especially given the level of sophistication in attacks against financial services.
In the need to reduce card-not-present (CNP) transaction fraud losses, behavioral analytics and machine learning is key to prevent fraud while providing a seamless cardholder experience. Machine learning can provide the insights that can foresee and prevent future breaches, helping financial institutions continuously improve their services and offerings to get a leg up on competitors. By utilizing machine learning within financial services to combat fraud, we can learn from and adapt to suspected fraud in real time to significantly minimize the space for fraud using the same card or device. Machine learning has the distinct benefit of being able to recognize patterns in data as well as spotting inconsistencies.
Artificial intelligence (AI) tools which learn and monitor users’ behavioral patterns for anomalies and warning signs of fraud attempts and occurrences are also becoming more commonplace in fighting crime. AI coupled with machine learning is already used within financial services to detect fraud by flagging unusual transactions and by using machine learning financial institutions can provide real-time protection and plans to mitigate fraudulent attempts, card holder and issuer risk.
Additional machine learning benefits include:
Protecting users in real time
Through machine learning, financial services institutions are able to flag possible fraud faster, allowing them to respond quickly according to their risk sores and fraud thresholds. The data gathered can help banks and issuers quickly identify fraud versus non-fraud and immediately either demand additional authentication or simply shut down a transaction or access to accounts and flag suspicious activity to the account owner.
Ongoing improvements for future mitigation
The knowledge gained by tracking and analyzing the information gathered around data breaches and fraud attempts, provides institutions the opportunity to improve their security strategies and implement stronger protocols and procedures, closing the gap for future attacks. By applying analytics and machine learning to a collective data set, there is stronger intelligence to prevent future fraud attempts.
Of course, fraudulent payments have a big effect on the bottom line for financial organizations. Measuring risk is critical, and it is at the top of every financial institution’s mind to detect what is fraudulent and what is not, as quickly as possible before a lot of financial damage is done. Data science helps payment security through identifying the fraud from the non-fraud, increasing top line by reducing friction during transactions and increasing the bottom line by decreasing fraud losses, all through machine learning, artificial intelligence and analytics.
Security in finance is a global challenge, and one that should be approached with thoughtfulness and a strategy to use the data we have at our fingertips to better understand the challenges, risks, and areas for improvement. There will never be a perfect solution, but the more advances we make in building in AI and machine-learning, the harder it will be for the bad guys.