Predictive analytics is a robust detection and prevention tool because it helps business leaders forecast when fraud will occur, where it will come from and who will commit it. How can entrepreneurs use this technology to improve their current tactics?

1. Leverage Data Science Techniques

Fraudulent activity can devastate smaller firms, whether it’s identity, financial or corporate-based. According to the Defense Logistics Agency, a typical case causes $8,300 in monthly losses — and lasts about a year before detection. Unless companies have nearly six figures in liquid assets, weak detection methods could be ruinous.

Leveraging data science techniques for predictive analytics can help business leaders forecast fraud before it occurs, minimizing losses. Decision trees are particularly useful because they operate on simplistic if/then rules, where each condition branches out into several possibilities. They can determine how the event will affect them with enough data points.

2. Forecast When Fraud Will Occur

Another way to use predictive analytics for detection is to determine how soon the next instance of fraud will occur. Although many businesses conduct internal audits, those catch just 25% of cases because fraudulent activity is often committed by the person they trust the most or suspect the least.

An artificial intelligence-driven tool can use market, demographic and account activity data to produce results in real time. This technology’s rapid response time and high accuracy level may explain why its market value in banking will surpass $64 billion by 2030.

A neural network can rapidly analyze massive datasets to identify hidden patterns and make context-specific predictions within minutes. It can even initiate prevention tactics like making reports or closing accounts. Notably, its speed and real-time processing are crucial since many fraudsters alter, falsify or destroy evidence soon after the act to hide their tracks.

3. Assign Risk Levels to Third Parties

Third parties are common sources of fraudulent activity. Despite this fact, many decision-makers neglect to establish proper governance frameworks. According to one global economic crime survey, 29% of senior executives admit their company doesn’t assign a risk score to each vendor — and 13% have no risk management strategy for them.

Logistic regression is a statistical modeling technique. Business leaders should use it to determine the probability of a binary outcome, meaning whether fraudulent activity will occur. They can calculate how likely each vendor is to commit fraud using a dataset with one or more variables. This way, they can improve their prevention and detection tactics simultaneously.

4. Predict What Type of Fraud Will Occur

Forecasting the type of fraudulent activity with the highest likelihood of happening is essential since each has varying risk levels, damage and odds of appearing. For instance, approximately 33% of corporate fraud goes undetected. This is a substantial amount, considering this type of deception destroys 1.6% of equity annually, the equivalent of $830 billion in 2021.

5. Determine Customers’ Risk Level

Of course, fraud doesn’t exclusively happen internally or through third-party vendors. Sometimes, bad actors commit it by taking over customers’ accounts or tricking them with social engineering tactics. According to the Federal Trade Commission, consumers collectively lost $8.8 billion to these deceptions in 2022, a 30% increase from 2021.

Business owners can use predictive analytics to track how at risk customers are for fraud. These efforts can help them maintain their brand reputation and retention rates. Anomaly detection techniques are ideal in this scenario because they identify and flag unusual behavior that signals fraudulent activity. Any substantial deviation requires intervention.

6. Determine Which Method to Select

Prevention is critical for entrepreneurs who lack an abundance of liquid assets for recovery. Fraud affects smaller businesses the hardest. For reference, small financial technology companies lose 57% more revenue than larger firms. On average, that accounts for 2.2% of their annual revenue, equivalent to $200,000.

One effective way to use predictive analytics to improve fraud prevention is by utilizing it to select the technique that will prove most successful. Forecasting which one will be most effective streamlines the decision-making process. This way, they can find a business-specific solution that maximizes their strategy’s efficacy.

7. Pinpoint Where Fraud Will Come From

While many people have their login information stolen or fall victim to phishing, some commit fraud themselves. In 2022, friendly or first-party fraud was responsible for the largest share of e-commerce companies’ fraud losses. They often created fake accounts or initiated dishonest chargebacks.

How can decision-makers determine which accounts are legitimate and which are fraudulent? A network analysis evaluates the characteristics and relationships of each entity — such as transactions, accounts and interactions — to uncover patterns. This way, they can determine if someone is committing fraud, even if no anomalous activity occurs.

8. Determine the Likelihood of Internal Fraud

Although entrepreneurs surround themselves with people they trust, fraud often comes from those they least suspect. Unfortunately, approximately 23% of startup failures result from dishonest founders or employees. While this figure covers everything from misrepresented financial stability to outright embezzlement, it highlights the importance of due diligence.

Decision-makers should use a natural language processing model to determine who will likely deceive or steal from their employer. By analyzing text data for sentiment, context and keywords, they can determine who possesses opportunity, motive and rationalization factors — strong indicators of their predisposition to commit fraud.

Predictive Analytics Involves an Ongoing Strategy

Companies can only make accurate predictions if they use relevant, up-to-date information. Even if they successfully improve their detection and prevention efforts after their first forecast, they should continue using predictive analytics periodically. Since fraud isn’t a one-and-done event, this shouldn’t be, either.