The Importance of Fraud Detection and Data Analytics

As financial crime is increasingly easier to perpetrate with today's technology-driven society, financial institutions and FinTechs are at a greater risk for fraud, money laundering, and other nefarious activities than ever before. One strong tool to combat such activity is known as fraud analytics—a combination of big data use and machine learning analytics. Fraud analytics are able to help detect, prevent and assess dangerous activity in real-time.

This article will discuss the importance of using fraud detection and data analytics for your financial institution or FinTech to operate securely and compliantly; potential access points of fraud analytics and how your organization can integrate them into effective AML compliance programs.

What Is Fraud Analytics?

Fraud analytics is the application of statistical, machine learning, and data mining techniques to identify irregularities, patterns, or behaviors that may suggest fraudulent activity. Such findings come from data sets including but not limited to transaction data, user history, behavior, geo-location data, etc.

Fraud Analytics are Comprised of:

Descriptive Analytics: Examination of Historical Data for Fraud Trends.
Predictive Analytics: Machine Learning Assessing Chance of Fraud.
Prescriptive Analytics: Recommended Action of Fraud Occurrence.
Real-time Capabilities: Immediate Notifications and Detection of Anomalous Behavior.

Fraud Detection and AML Compliance Relationship

Fraud detection is vital in safeguarding company resources and reputation, and it aligns with AML compliance. Fraudsters looking to disguise the trail of their illicit activities often commit fraudulent behavior with the intention of using their findings for more criminalistic activities; thus, the need for analytics for AML compliance is even greater.

How Fraud Detection Can Enhance an Effective AML Program

Detect Threats Earlier: Potential warning signs are identified prior to them occurring.
Ease Regulatory Compliance: Ongoing AML/CFT requirements necessitate automated monitoring and regulatory filings which can be facilitated through fraud detection analyses.
Enhance Cost Efficiencies: Minimizes organizational and external costs related to fraud and loss of reputation.
Expedite Investigative Procedures: Provides investigators with enriched, contextual information for fast case closure.
Where Fraud Analytics Can Be Utilized in Financial Services

For Canadian businesses, aligning fraud analytics with AML frameworks can significantly strengthen internal controls. Explore our CAMLO/MLRO services to ensure your compliance team has the right tools in place.

Fraud analytics can be utilized in financial services for the following purposes:

1. Transaction Monitoring

Alerts should be generated for any type of transaction that would not seem ordinary. Fraud analytics can create these flags and then develop an associated risk score through predictive modeling.

2. Suspicious Activity Reports

If a regulated entity must submit a suspicious activity report (SAR), it should be supported by other data if applicable to better support its filing. Fraud analytics can suggest the most appropriate SAR supporting documentation.

3. Examinations and Audits

Any type of internal audit requires supporting documentation and forensic testing to determine adherence or non-adherence to policies and procedures. Fraud analytics can assess the validity of such inquiries.

4. Anti-Money Laundering (AML) Compliance Determination

If a regulatory body questions a regulated entity, it must provide compliance deficiencies. Fraud analytics can demonstrate whether the entity should create a monetary penalty recommendation to proactively start the process.

In cryptocurrency and virtual asset environments, blockchain analytics plays a pivotal role in identifying suspicious wallet behavior. Learn more about our token due diligence services.

Financial services firms and FinTechs utilize fraud analytics in multiple forms:

Payment Fraud Prevention

Behavioral analytics and geolocation allow for real-time scoring of every transaction to potentially identify fraudulent activity.

Identity Fraud Prevention

Fraud analytics relative to KYC documentation and biometric authentication, combined with various forms of analytics, help companies assess whether users had their identity stolen or if they utilize synthetic identities.

Employee Fraud Prevention

If an employee or third-party contractor logs into an account from a different country or approves a transaction far outside the norm, fraud analytics can call attention to such behavior.

Cryptocurrency AML Compliance

Fraud analytics solutions can be leveraged by virtual asset service providers to identify large virtual currency transactions and meet requirements of Law Enforcement and Virtual Currency Transaction Report (LVCTR) requirements.

Fraud Analytics Drawbacks

Fraud analytics is not without its drawbacks:

Data Integrity and Data Silos. Should the data be generated inconsistently or pooled into too many silos, effectiveness can be diminished.
False Positives. Too many false positives can create alert fatigue in compliance departments and stretch already limited compliance resources.
Fraudsters are Fast. As criminals operate fast, fraud detection needs to train the model consistently to keep up with constantly changing criminal enterprises.
Regulatory Variance. Not every jurisdiction requires compliance from analytics in a like manner.
A thorough effectiveness review of your current AML and fraud detection tools may highlight possible pitfalls and how to circumvent them.

A thorough effectiveness review of your current AML and fraud detection systems can help identify and mitigate these challenges.

How AML Incubator Facilitates Fraud Analytics Integration

AML Incubator helps FinTechs, MSBs, and VASPs integrate cutting-edge fraud analytics into their compliance programs. From a blank slate to adding to an already existing compliance program, we'll ensure your systems are intelligent, scalable, and ready for audit.

We assist with:

Regulatory remediation of legacy systems
Integration with blockchain forensics
Tailoring of customized fraud risk assessments

Explore our regulatory remediation services or contact us for a tailored consultation.

Detecting fraud via innovative analytics is no longer a nice-to-have. It's a necessary piece of any financial crime prevention strategy and accompanying AML compliance. As fraudsters get smarter, we must become craftier at identifying risk and applying timely intervention.

When companies invest in fraud analytics, they become more resilient while increasing the overall financial ecosystem's resiliency and protection.