Financial crime in the U.S. alone tops $300 billion every year – and the vast majority of it goes undetected.
That’s no surprise considering the challenges companies face when chasing fraud. Bad actors constantly change their increasingly sophisticated tactics, and there's a need to contend with historically siloed information that hobbles the ability to connect the dots. However, many tools at their disposal deliver too many false positives or take weeks to yield critical insights.
By widening the funnel and quickly combining and processing more data sources and signals, it's possible to uncover genuinely high-risk activities quickly. Identifying the highest priority cases requires a holistic approach based on three key technologies: entity resolution, network generation and advanced analytics.
The foundation of a modern fraud detection strategy starts with the ability to resolve all entities (people, addresses, businesses, transactions, devices, etc). This allows an institution to create a single view of every customer by connecting critical but siloed data. Most companies store information about transactions, KYC (know your customer) and corporate registries in separate business units - without sharing it across the organization. Entity resolution is the key to squeezing value out of missing or less-than-perfect data by integrating all information about a person, place or thing from inside and outside the bank into a single view of the truth.
The next step adds critical context to the data through network generation technology. It enables organizations to process and visualize customer or employee relationships and behaviors. This contextual layer changes the way organizations understand risk and detect suspicious activity. For example, a call center employee accessing accounts outside their geographic area may not raise a red flag in isolation. But the picture looks quite different if a bank can overlay third-party fraud events on those accounts and observe a concentration pattern.
The final pillar is advanced network analytics, and when applied to resolved entities and networks, it can provide the full context of customers, their behaviors and their networks. That means detecting anomalies in real time, reducing false positives by understanding mitigating circumstances in the transaction data, and significantly accelerating the decision-making process.
To see this phenomenon in action, consider mule activity, where individuals use their own information or accounts to obtain something of value for a fraud ring. In 2020, mule activity increased by 41% in 2020, according to Aite Group.
To identify mule accounts, financial institutions would typically assess individual accounts for red flags by looking for signs of financial distress or sudden changes in an account profile. The information is often fragmented across the institution and does not provide enough context to enable definitive insights into how the account is being used.
But a more granular picture emerges when the bank can combine all information at its disposal, both internally and externally to build a single customer view, which is then used to enrich the transactional data with critical contextual information. Network intelligence and advanced analytics can then help to assess and identify patterns that are indicative of a mule network of drop addresses that are being used across several account holders with no obvious relationship. Transactional information may also be analyzed using analytics to surface the presence of an inverted pyramid flow of funds from multiple mule accounts to a single account (many to one scenario).
Financial crime isn’t going away, and it’s growing worse all the time. That’s why institutions need to move at the speed of fraud. That requires the ability to roll out detection models and assess information within minutes and hours, not weeks or months.
By harnessing the power of entity resolution, context and analytics, financial institutions can identify risk faster and more accurately, reducing their fraud losses and risk exposure and improving their bottom lines.
