Monday, January 18, 2021

Artificial Intelligence in Fraud Management

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Financial Fraud Action UK estimated fraud losses in the UK totaled £768.8 million in 2016, an increase of 2% compared to 2015.

Fraud is a billion-dollar enterprise, with impacts on individuals, business and governmental organizations.  Frauds is an evolving crime, that requires complex and sophisticated data techniques to detect and prevent.

Traditionally, fraud detection and prevention rely on access to a vast number of customer records, cross-sector database sharing, and knowledge domain.

Time-consuming data analysis and modeling have proven to be a step behind in the detection and prevention of sophisticated fraud crime.  The battle to overcome fraud crime is beginning to shift businesses towards advance artificial intelligence.

Artificial Intelligence is not new in the fight against fraud crime, albeit lower level of artificial intelligence in the form of data modeling or rule-based models with the capability of alert triggering.

Related: Fraud Detection and Prevention Market to hit $80 Bn by 2025 – Report

The rules mimic expert decision-making processes and providing a score on the likelihood of fraud.  Without intervention, the rules will be in place for as long as required thereby becoming ineffective in keeping up with trends or changes in fraudulent behaviors.

Over the past few years, Advanced Artificial Intelligence has opened the door to machine learning providing advanced algorithms and capability to deal with a large amount of data.  Also, the flexibility of Advanced AI gives a positive outlook on the fight against fraud crimes.

The surge in online banking has necessitated the need for better and sophisticated fraud solutions.  Digital banking comes with its own issue of anonymous fraud crime, with increasing attack on this channel by tech-savvy criminals.

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Companies are having to invest in advance artificial intelligence systems with capabilities of self-learning and self-calibrating for real-time analysis and hopefully better rate of detection and prevention.

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