Forrester: Adopting fraud-fighting AI requires the right technical framework

AI tools have become part and parcel of fraud management solutions in the enterprise, according to a new Forrester report. In it, analysts at the firm identify key fraud management use cases where AI can help, mapping how brands can deploy AI technologies in each scenario.

Fraud is on the rise worldwide, and it’s impacting enterprises’ bottom lines. According to a PricewaterhouseCoopers survey, 47% of businesses experienced a fraud in 2019 and 2020, which cost a collective $42 billion. Some studies show that the pandemic is playing an increasing role. In a report commissioned by J.P. Morgan, nearly two-thirds of treasury and finance professionals blamed the pandemic or the uptick in payments fraud at their companies.

The Forrester report outlines AI’s unique strengths in combating fraud, including its ability to boost monitoring accuracy, augment human intelligence, and bring biometrics into the mainstream. Feeding rich datasets into fraud detection AI models can help spot fraud that rules-based systems overlook, Forrester notes, while those same models can be used by security teams to prioritize which alerts to investigate. Meanwhile, emerging AI-based solutions like biometric verification enable app authentication in near real time.

The report stresses, however, that organizations need to have the right technical pieces in place to reap the benefits of fraud-detecting AI. For example, prediction models and risk scoring systems must be low-latency to cope with large transaction volumes. Beyond this, data scientists must build training, test, and validation datasets, which can be challenging. According to Forrester, many banks report that their training data is miscategorized and suffers from quality issues like missing fields or inconsistent spellings of people and organizations. And in Asia, banks are reluctant to provide training data to consulting firms — and sometimes even to employees.

Indeed, other reports show that data issues plague companies of all sizes on their AI journeys. In an Atlation survey, a clear majority of employees (87%) pegged data quality issues as the reason their organizations failed to successfully implement AI and machine learning. McKinsey similarly estimates that companies may be squandering as much as 70% of their data-cleansing efforts.

Overcoming challenges

Organizations must also ensure that models evolve over time and give reasons to explain why certain transactions are deemed fraudulent, Forrester says. They also need iterative, closed-loop workflows to train models and integrate them with data sources like blacklists, whitelists, device IDs and reputations, and know-your-customer lists.

The report recommends that companies use a combination of on-premises and cloud-based implementation options for their AI fraud detection use cases. While on-premises solutions offer convenience, the cloud has the potential to improve training and inferencing performance, Forrester notes, lowering costs and in some cases providing enhanced protection.

“As more fraud management vendors offer cloud-based solutions, it makes sense not to invest solely in static on-premises hardware and software, but to augment their on premises assets with elastic computational resources in infrastructure-as-a-service or software-as-a-service solutions,” coauthors and analysts Andras Cser, Danny Mu, and Meng Liu wrote.

Once barriers to the adoption of fraud-fighting AI are overcome, the advantages can be enormous. Visa prevents $25 billion in annual fraud thanks to the AI it developed, SVP and global head of data Melissa McSherry revealed at VentureBeat’s Transform 2021 conference. And at a higher level, aggregate potential cost savings for banks from AI applications has been estimated at $447 billion by 2023.