AI-Powered Risk Scoring Helps Fight Payment Fraud

2020 was a transformative year for online services, commerce and payments. On the back of the pandemic, the digital commerce ecosystem has experienced a radical growth spurt. The widespread disruption across all walks of life led to a tectonic shift in e-commerce, speeding up the adoption of digital wallets, peer-to-peer transfers, and mobile banking — and to a parallel decline of cash payments at point of sale. Covid forced banks and fin-techs to implement innovations in financial services that were expected to require a decade to complete – within just a year.

A breeding ground for sophisticated fraud

The downside of the proliferation of new digital payment channels and technologies has created a breeding ground for sophisticated fraud opportunities and bad actors, posing new challenges for fraud detection, prevention and prediction. According to the IC3 (Internet Crime Complaint Center), financial losses caused by fraud in 2020 were at its highest ever, exceeding $4.1Bn – a 17% increase from 2019. The number of complaints reported to IC3 during last year – nearly 800,000 – also set a new record.  

 A combination of factors make the fight against payment fraud so challenging: 

  • Large volume of transactions. The growing volume of digital transactions and payments are putting pressure on fraud detection and prevention technologies that need to process, analyze and authorize a larger volume, variety and velocity of payment traffic. 
  • Bad actors are becoming increasingly sophisticated. In the constant arms race between fraudsters and financial organizations, criminals are adopting sophisticated tools and cutting-edge cyber tactics to overcome legacy security systems.   
  • Difficulties in identity verification. Identity theft is a critical form of cybercrime, putting both individuals and enterprises at risk. The most common types of identity theft are real name theft, synthetic theft, and account takeover – where the stolen information is used to register fraudulent new accounts, whether banking checking accounts, credit card account, or mobile phone accounts.

Legacy security systems struggle to keep up with a new breed of financial crime 

Banks and other financial institutions are fighting back against financial fraud in all its manifestations. In fact, according to AI research group Emerj, approximately 26% of the venture funding raised for AI in the banking industry is for fraud and cybersecurity applications, more than any other use-case category. That’s because current solutions cannot effectively deal with the new digital payments reality, for a variety of reasons: 

  • Dynamic data challenges.  The current payment ecosystem is too complex and fragmented for manual management and a siloed data approach. Rule-based legacy systems with fixed outcomes struggle to keep pace with the challenges of dynamic data, and cannot be efficiently scaled-up to adapt to the new reality. 
  • False positives. Facing a surge in digital transactions, threshold-based systems that cannot autonomously adapt to changing data scenarios send out a high number of false positives and unsubstantiated alerts, leading to alert storms, errors, and increased pressure on manual remediation of each of these alerts.
  • False negatives. For the same reason, traditional systems struggle in identifying false negatives, or “unknown unknowns”, often missing instances of fraud.   
  • Manual investigation. Organizations lose ample time and resources through lengthy investigations that are conducted with partial data and are also prone to errors and biases. 

 AI is a game-changer in combating financial fraud  

The new generation of AI solutions for risk management and fraud detection, prevention and prediction use sophisticated AI risk-scoring systems for flagging fraudulent payments. These technologies are fundamentally different to rule-based solutions in that they learn from historical fraud patterns and recognize them in future transactions. Machine Learning algorithms are also significantly more effective than humans in spotting suspicious transactions and fraudulent traits within huge volumes of structured and unstructured data.

The benefits of implementing an AI solution in mitigating payment fraud: 

  • Complete monitoring of dynamic and changing data. AI is very effective at working with large, complex time series and tabular data sets using anomaly detection, and is cut out for present day fraud. By monitoring all data sources and correlating across domains and metrics, AI algorithms for fraud detection can more effectively identify suspicious activities and evolving fraud patterns. 
  • Reduced false positives, false negatives and alert storms. Supervised machine learning employs alert reduction mechanisms that dramatically cut false positive alerts and alert storms, enabling teams to focus their resources on the alerts that count. In addition, users see a decline in the number of legitimate transactions that are wrongfully declined. On the flip side, AI systems effectively capture false negatives and reduce the number of fraud instances, even when bad players use new methodologies and schemes
  • Identification of fraud patterns with human in-the-loop feedback. Feedback from users working on the AI system is fed back to the algorithms, enhancing their ability to identify new fraud patterns, features and dimensions of fraud. 
  • Lowering costs of fraud prevention. Risk scoring and fraud prevention solutions for financial institutions bring significant ROI through several channels. While reduced fraud-related monetary damages is the main advantage, these technologies also contribute to the bottom line by reducing costs associated with manual fraud investigations, increasing customer satisfaction, and reducing customer churn.  

BeyondMinds brings scale, speed, and explainability to risk scoring 

With more and more retail activities and services delegated online, businesses and organizations will need to continue to improve at fighting payment fraud to prevent monetary damages, comply with legislation, and deliver customers the secure payment performance they expect. BeyondMinds provides financial institutions with advanced payment fraud detection capabilities that leverage advanced analytics and machine learning to: 

  • Analyze daily transactions, sending alerts once significant changes in payment patterns are detected 
  • Reduce false positives dramatically 
  • Discover new fraud patterns  
  • Explain why certain payments were flagged as suspicious 

BeyondMinds’ AI solutions are hyper-customized around each financial institution’s data, business needs and use cases. The solutions delivered by BeyondMinds are enterprise-grade, with features including auditability, explainability, and compliance. The production-ready solution enables organizations to scale their AI transformation and rapidly create ROI from AI. While many other AI solutions can take months or years to successfully reach production, the BeyondMinds platform enables companies to achieve a production-grade AI risk scoring and fraud detection solution within just 10 weeks