Optimising the effectiveness of fraud management through digital transformation
It’s no secret that financial crime and fraud are a major issue for both businesses and consumers, and one that is due to grow rapidly in the coming years. In 2023, fraud represented 40% of all crime committed, costing an estimated £6.8 billion to society in England and Wales.
This situation is only being made worse due to the cost-of-living crisis, as consumers are more likely to take risks, such as making dubious ‘too good to be true’ purchases online, accepting opportunities that offer quick payouts, or falling for romance scams as individuals feel alone and isolated.
The rapid advancement of technology, in particular the increasing accessibility of Generative AI, makes scams more realistic and security easier to breach. For example, it may be exciting and fun to create fake images of yourself with interesting backgrounds, but in the eyes of fraudsters, that’s fake IDs, synthetic documents, and an opportunity to create fake bank accounts.
While these challenges are significant for financial institutions, it’s also crucial that consumers h the best user experience possible and a smooth customer journey. So, how do we strike the best balance?
Using technology to fight financial crime
The integration of Artificial Intelligence (AI) and Machine Learning (ML) in fraud detection and prevention systems has advanced the fight against fraud. Anomaly detection algorithms and classification models that assign probability scores are now more common, helping banks reduce the number of fraudulent transactions that occur.
AI solutions can uncover trends that are invisible to humans due to the massive data volume and the imbalance between fraudulent and genuine transactions.
Although AI solutions are key enablers in the fight against fraud, they lack adaptability and transparency, making it difficult to react to new fraud threats or understand high-risk scores.
Rulesets remain crucial, offering control over the organisations risk appetite, alert volume and end user experience. They are easily interpreted, auditable, and quickly adjustable.
The question is, how do we make the most of both technologies?
What can financial services do to better protect their customers?
By combining the two approaches discussed, organisations can create a hybrid approach to combatting fraud and scams. This involves creating rulesets that factor in AI and ML based risk scores, resulting in transparent, adaptable and accurate solutions. This methodology, already in use across different organisations, is seen to be far more effective than using either technique alone.
However, combining rulesets with AI can become very complex, with millions of possible combinations and permutations of rulesets available. Adjusting rulesets to adapt to fraud trends or business needs requires changing thresholds, a task often addressed by data science techniques such as Grid Searches, Bayesian Optimisation and Reinforcement Learning.
This complexity can lead to outdated, ineffective rulesets. To avoid this, it’s better to redesign rulesets for the current fraud patterns rather than fine-tuning them. So yes, we can continue to tune the ruleset, but we are better off redesigning the ruleset for the intended purpose at that moment in time.
Automating the design and construction of these rulesets with AI retains the benefits of hybrid systems and ensures they remain dynamic and effective.
This new technology provides a complete end-to-end optimised fraud management process, allowing financial services organisations to stay in control, and deliver the very best service for their customers.
Find out how this technology can help you achieve your fraud goals.
Ella Gago-Brookes
Ella joined techUK in November 2023 as a Markets Team Assistant, supporting the Justice and Emergency Services, Central Government and Financial Services Programmes.
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