Billions of cross-border financial transactions flow every second, resulting in a massive digital trail. So, criminal groups leverage this scale for their illegal operations. Their actions range from laundering money and circumventing global sanctions to funding trafficking and terrorism. Therefore, for enterprise risk management executives, the challenge is not a lack of data. Instead, it is about piecing together an enterprise-wide view of that data from a myriad of disconnected sources. That is the promise of entity resolution, and this post will describe how it can help fight financial crime.
Bad Actors Attempt to Bait Auditors & Financial Crime Investigators
Cybercriminals deliberately introduce inconsistencies. Doing so thus lets them cover their tracks. Think of small discrepancies in the registered name, or partial changes to a residential address and date of birth. Such defects easily slip through rule-based screening checks.
Additionally, this situation leaves compliance investigators sitting for hours, piecing together separate records. That almost certainly results in critical investigative gaps. You are thus looking at inefficient operational flow and a slew of false positive alerts.
In response, institutions are implementing more advanced data disambiguation methods. They can consolidate internal intelligence in the face of many sophisticated evasion techniques. For instance, by leveraging specialized entity resolution services, organizations can seamlessly consolidate scattered data points into a single, comprehensive customer profile.
This method guarantees that all variants of disconnected identities, like “Jonathan D. Doe” and “J. Doe,” are identified as the same individual in all systems and partner databases.
The Technology of Threat Detection
Contemporary entity resolution is more than fuzzy matching. It applies smarter probabilistic matching, decades of context and behavior analysis. So, your team can identify the true probability that individual records are, in fact, referring to the same entity.
In the past, compliance teams used rule-based matching rules. However, they needed constant manual editing. Thankfully, the world of technology is moving from rule-based to AI and adaptive machine learning models.
In fact, financial institutions utilizing machine learning-powered entity resolution can report a higher match rate when dealing with inconsistent, poor-quality data sources. This also means compliance is shifting from managing alerts to preventing threats.
Relationship Mapping with Graphs
Circular flows of money, multiple shell companies, and network obfuscation allow fraudsters to hide their activities entirely. Therefore, network relationship mapping in a graph environment can help by “showing visually how people, corporate accounts, common IP addresses, and physical locations are connected,” said Markus Hartmann, a leading researcher in advanced artificial intelligence and graph analytics.
According to such a thesis, “Having a single, unified graph environment will give investigators the upper hand in discovering unseen risks.”
Natural Language Processing in Action
Structured, numerical data tells only part of the story in a risk assessment. Now, with NLP enabled, today’s enterprise platforms can tap unstructured data sources like negative media articles, internal emails, and raw transaction memos. That is why financial crime investigators can extract intelligence. They will swiftly uncover if a risky wire transfer can be linked to the individual in question in a foreign-language news report. An AI solution will identify that link immediately.
Industry-Wise Use Cases and Applications
Anti-money laundering (AML) teams within commercial banking are under tremendous pressure from regulators. The current theme is uncovering ultimate beneficial owners (UBOs). For reference, recent guidance issued by the FATF leans heavily on the importance of connecting related companies to reveal shell company networks.
Entity resolution thus helps meet this demand by quickly connecting complex corporate structures into a single map. That allows you to deliver a permanent record for regulators.
The insurance industry is also under threat from an entirely different, but equally pernicious, attack vector. Consider synthetic identity fraud. Here, criminals first create new identities, piecing together real and fake information to eventually build a whole new identity.
However, by implementing robust financial crime & compliance solutions, proactive insurers can continuously cross-reference behavioral transaction patterns and historical claims data. Doing so, therefore, dismantles these synthetic profiles before they trigger fraudulent payouts.
Key Players, Tools, and Market Trends
There are multiple smart software companies taking the financial entity resolution and crime tracking technology shift to the cloud. Organizations such as B2B software companies Lucinity, Data Walk, SymphonyAI, and Ripjar build next-generation intelligence platforms. They also integrate various data sources separated into silos within an organization.
These sophisticated platforms thus eliminate the need for reactive alert triage. Instead, they help create a culture of substituting them with proactive intelligence discovery.
Conclusion
For B2B executives, chief risk officers, and directors of compliance, digital inaction can be a costly pit. After all, clinging to a heavily fragmented data stack ensures a never-ending loop of ghastly alert fatigue. That also indicates exponentially rising running costs. Your risk mitigation plans, hence, need a funding line earmarked for building an infrastructure in response to new data and global threat vectors.
In the grand scheme of everything, investing in modern entity resolution is not simply another IT upgrade. Instead, with one unified single source of truth, modern financial organizations will be able to clear the investigative backlog, effortlessly meet international regulatory requirements, and create an infinitely more resilient financial crime defense.
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