You’re navigating a bustling city, where every street and intersection represents a different aspect of your organization—departments, transactions, vendors, and employees. On the surface, everything seems orderly. Traffic flows, people go about their daily routines, and operations hum along smoothly.
But what if, beneath this seemingly normal landscape, hidden connections and unseen patterns were telling a different story? What if, amid the routine movement, small clusters of individuals were engaging in repeated transactions at unusual times, forming a suspiciously tight-knit web?
This is where Social Network Analysis (SNA) and Artificial Intelligence (AI) come into play.
What is Social Network Analysis?
SNA is a powerful analytical method that examines relationships between entities—whether people, businesses, or even transactions—to detect unusual or high-risk interactions. This process maps out connections, identifies central players, and spots patterns that may otherwise go unnoticed.
In fraud detection, SNA can reveal:
- Collusion between vendors and employees
- Bid-rigging and collusion between vendors
- Unusual transaction frequencies between connected entities
- Shell companies created to funnel funds
- Hidden hierarchies that evade standard compliance checks
While traditional rule-based fraud detection systems might miss these nuanced relationships, SNA—when combined with AI—can expose sophisticated fraud schemes in real time.
How AI Supercharges Social Network Analysis
AI-driven SNA takes fraud detection to a new level by leveraging machine learning algorithms to vast amounts of structured and unstructured data. Here’s how AI makes SNA more effective:
Fraudsters are smart, but AI is smarter. AI algorithms have the ability to analyze vast amounts of data points to detect deviations from normal transaction patterns. For example, if a group of vendors suddenly starts submitting invoices for similar amounts within minutes of each other, AI can flag it as suspicious before a single payment is processed. Paired with metadata AI can help understand locations and common trends.
Criminal networks don’t advertise their connections. AI-powered SNA uncovers relationships between individuals and organizations that might not be obvious on paper. Whether through shared IP addresses, geospatial analyses, banking details, or even social media interactions, AI can highlight red flags that investigators might otherwise miss.
AI doesn’t just detect fraud—it predicts it. By learning from past fraud cases, AI models can anticipate new tactics fraudsters might use. This allows organizations to proactively strengthen controls before an attack occurs.
One of the biggest headaches in fraud detection is false positives—legitimate transactions mistakenly flagged as suspicious. AI refines SNA models to differentiate between harmless anomalies and actual fraud, reducing unnecessary investigations and improving efficiency.
AI and SNA in Action
Let’s put this into perspective with a real-world scenario.
A government agency overseeing grants and contracts is struggling with fraud. Every year, millions of dollars are lost to fake companies, conflicts of interest, and vendor collusion.
Step 1: Mapping the Network
SNA maps out relationships between vendors, contractors, and employees. AI then scans for irregular interactions—such as a contractor consistently working with the same procurement officer or a company that shares a bank account with multiple other vendors.
Step 2: Identifying Risk Indicators
Algorithm based risk scores can help illuminate known fraud patterns and allow you to rank your threats to better manage caseloads and resource allocation. If an employee’s personal email is linked to multiple vendors, that’s a red flag. If a contractor submits invoices from a country where the company has no operations, that’s another.
Step 3: Actionable Insights in Real Time
Instead of waiting for an audit months later, AI-driven SNA detects these red flags immediately, allowing investigators to take action before fraudsters get away with taxpayer dollars. Working with AI allows agencies to break away from the pay and chase cycle, focusing on near real time analysis.
How Organizations Can Leverage AI and SNA
To stay ahead of fraud, organizations must move from reactive detection to proactive prevention. Here’s how:
- Implement Continuous Monitoring – AI can analyze transactions in near real time, ensuring that fraud schemes are caught before they escalate.
- Leverage Cross-Departmental Data Correlation – Fraud often spans multiple departments. By integrating data across different systems, organizations can see the full picture instead of fragmented pieces. Bringing in the right stakeholders across federal, state and local departments to deal with the problem holistically.
- Customize AI Algorithms for Industry-Specific Risks – Fraud schemes vary by sector. AI models should be trained to recognize patterns relevant to each organization’s unique challenges.
- Use Algorithm-Generated Risk Scoring – Instead of sifting through thousands of alerts, teams can focus on high-risk cases flagged by AI-driven risk scores.
Gone are the days when fraud investigations relied solely on manual audits and tip-offs. With AI-driven Social Network Analysis, organizations can uncover sophisticated fraud schemes faster, with higher accuracy, and at a fraction of the cost.
At TrackLight, we combine AI-powered analytics, risk scoring, and Social Network Analysis to help organizations detect and prevent fraud before it happens. Because in today’s digital landscape, staying one step ahead of fraudsters isn’t just an advantage—it’s a necessity.
