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Machine Learning Is Placing Risk Managers On Fraud's Front Lines

April 27, 2016 at 2:46 PM

Few take on a larger portion of the responsibility to steer their organisations to success than Risk Managers. And with fast-moving consumers, a globalised marketplace, unabated industry disruptions and shifts seemingly all occurring in unison, modern Risk Managers face a new set of challenges to that of their predecessors. But now, banks and other financial institutions are using historical customer transactional data to detect unusual activity on buyers' debit and credit cards to freeze transactions until purchases can be verified by the card owners.

Also, identifying common characteristics of insurance fraud through machine learning is reducing the manpower required to investigate claims and minimising risk in the process. And considering that across-the-line fraud in the US alone is estimated at around $80 billion per annum, the need for a proactive stance against this criminal activity is quite apparent.

Separating friend from foe with automated decision-making

Machine learning algorithms applied to insurance claim data are enabling insurance companies to automate fraud detection. For customers with legitimate cases, this significantly shortens turnaround times as above-board claims can then be processed in much shorter time spans, thus increasing customer satisfaction by speeding up the payment of legitimate claims.

Here is an insightful whitepaper on how data analytics is automating the detection of fraud and giving businesses a stronger foothold in the ongoing battle against fraud.

By actively learning card owners' buying patterns  and building consumer profiles based on this information, predictive models are making near real-time decisions on debit card transactions that may or may not be fraudulent. This enables risk management teams to take a more preventative stance in their ongoing combat against fraud. Considering the vast amounts of transactions that occur globally every minute, it's simply impossible for humans to analyse every transaction, highlighting the unique advantage that machine learning provides risk teams in preventing fraudulent activity on consumers' debit cards.

Here are 3 ways Risk Managers should be using Big Data.

As machine learning algorithms continue to learn how to distinguish normal transaction parameters from abnormal ones, companies across the board will become better able to prevent fraud instead of recovering from its aftermath.

Shifting from a reactive to a proactive stance

For years, companies were content to look at their data through a retrospective lens, but advances in technology has shifted the current way of thinking. Machine Learning is a way to help shift our focus from what happened to what is most likely to happen, and in so doing, empowers us to take the right pre-emptive or corrective action that serves the need of the business. From the world of sport, to business operations, to product development, and even the newspaper industry, predictive analytics is making its impact felt far and wide.

For more information on how Principa's data analytics platforms are helping business across Africa leverage untapped value in their data assets, contact us to learn more about how we make data work wonders.

predictive analytics guide

Blog post originally published 13 January 2016.

Image credit: http://www.forexautotradingrobots.com//

Robin Davies
Robin Davies
Robin Davies is the Head of Product Development at Principa. Robin’s team packages complex concepts into easy-to-use products that help our clients to lift their business in often unexpected ways.

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