Redefining Fraud Risk Management: Quantitative Solutions in the Face of Emerging Threats

Digital channels have become the mainstream mode of transaction for almost all businesses today, big or small. They have become the most preferred forms of payment for most consumers and are transforming how businesses operate. The pandemic acted as a catalyst, accelerating e-commerce and digital adoption by otherwise traditional businesses, and this trend is here to stay. But as the digital tide rises, so do the stakes. With an exponential surge in online transactions comes an increased risk of fraud and security breaches, directly threatening the fundamental pillars of a digital business - trust, personal data security, and customer perceptions and experiences.

Fraud victims have reported feeling anxious, displeased, and frustrated when warned about potential fraud.

Highly sophisticated, large-volume fraud attempts have introduced significant challenges in creating positive customer experiences, even as customers demand low-friction, more streamlined journeys, and quick transaction completions. On the flip side, the evolving threat landscape requires risk mitigation, fraud deterrence decisions, and protective actions to be taken in real time. This, however, can be difficult considering:

1. Human cognitive limitations - With significant digital adoption and penetration, it is impossible for fraud experts to manually study the endless list of data touch points and fraud events to identify potential risk patterns and derive intuitive fraud prevention rules and interventions.
2. Siloed fraud monitoring - In online marketplace transactions, a spike in purchase activity is only suspicious and anomalous if there is no corresponding spike in order activity. In other words, anomalies are anomalies only in a particular context. Therefore, looking at individual transactions through the lens of fraud rules may not suffice; they are just false starts. There is a genuine need for risk and fraud analytics that pull data from across the transactional platform environments for threat analysis.
3. Rapidly evolving cyber threats - Digitization has resulted in a steady surge of fraud attempts, the volume of which is overwhelming to detect with traditional fraud management techniques and rule-based controls. These shortcomings call for developing and implementing unsupervised learning models that detect emerging fraud patterns effectively.

The need for digital businesses to perform a balancing act between fraud deterrence and enriching customer experiences is high. How can companies ensure secure, smooth, and quick transactions for their customers? Adopting a quantitative approach and using data analytics to complement existing qualitative and instinctive considerations reduces risk exposure across diverse fraud vectors.

Now to the tough question: how do businesses establish a holistic and pre-emptive ecosystem for risk and fraud deterrence? Businesses, irrespective of their level of maturity in the analytics space, can benefit from analytics consulting partners who bring in a structured approach and cross-industry exposure to establish this ecosystem successfully. Let's break down the process into manageable steps:

1. Understand Risk Susceptibility: It begins with understanding the various areas and channels exposed to fraud elements through continuous channel risk profiling initiatives. These include risks associated with new product development, existing product feature enhancements, business process redesign, workflow improvements, and expanding segment coverage.

 

2. Identify Threat Intelligence: The next step would be identifying and classifying the possible data sources to gather threat intelligence. Sources could include.

•    Internal: historic fraud logs, false positives, etc.

•    External: fraud controls, negative files, trends & red flags, offender lists, social

•    Industry/Sector: payment partner warnings

•    Regulatory: compliance notifications, federal risk flagging, cyber threats

•    Law Enforcement: criminal history files

Establish how this data can be utilized to create auto-rules and auto-decisions for risk signals, considering data completeness, accessibility, quality, and addressing data privacy or regulatory compliance concerns.

3. Define Actionable Strategies:

•    For Data Activation: For all the identified data sources, classify the data in scope, define the data capture and ingestion strategies, establish data access by the risk and fraud models, establish data consumption and access rules.

•    For Advanced Analytics Use Case Development: Decide on the extent of unsupervised and supervised modeling to be used, the level of data science involvement in the solution, and reuse opportunities.

4. Enhance Risk & Fraud Predictability: Develop fraud detection and prevention mechanisms, including fraud rules, risk scoring algorithms, machine learning models, and analytical solutions, starting with unsupervised models to detect fraud events and curate confirmed fraud patterns.

•    Analyze fraud instances tagged by the unsupervised model to create new risk rules.

•    Alternatively, suppose the fraud is too complex to be detected by rules. In that case, a supervised learning model can be introduced to detect the said instances, in which case the tagged fraud instances can be used as training data to train the model.

5. Improve Risk & Fraud Operations: And finally, it is essential to:

•    Determine risk & fraud appetite and set objectives, which include articulating the level of risk the organization is willing to take, defining the guardrails for fraud control mechanisms in terms of acceptable level of fraud losses, impact on the customer experience, and effectiveness of business operations.

•    Continuous risk assessment and review, identify theft attempts, account take-over, transactional frauds, internal collusion risks, partner or vendor fraud, and application loopholes. 

•    Periodic outcome review and action that include monitoring, issue identification, issue reporting, decisions, and resolution of fraud policies and protocol gaps. Revisit KPIs, fraud metrics monitoring & measurement tactics.

Risk & fraud analytics are crucial for digital businesses to maintain credibility and earn consumer trust.

Adopting a data and analytics first approach will enable businesses to analyze all transactional and intelligence data to derive meaningful risk management insights at scale, implement automated fraud detection and interventions to deliver improved fraud predictability in real-time, ensure fail-proof deterrence against risks, all the while ensuring low friction on customer experience.

With the right approach to fraud risk management, businesses safeguard their integrity and cultivate an environment where customer experiences are enriched and protected.

Author: Karthik Ravichandran – Manager, Consulting Services, LatentView Analytics