How to Optimize Fraud Detection to Reduce Risk Exposure


Fraud detection systems categorize applicants based on historical data. While optimized for accuracy, not all platforms mitigate risks due to misidentifying individuals as fraudsters. High rejection rates can hinder business growth and increase operational risk.

However, Instnt’s risk models balance fraud risk with your organization’s growth ambitions. The fraud loss protection insurance transfers up to $100 million in liability for each accepted user, allowing your company to boost digital sign-ups while decreasing risk.  

By optimizing your fraud detection strategy, you can minimize exposure, improve acceptance rates and protect your company from financial loss. Discover the technologies and methods to improve your system's accuracy and effectiveness.

Effectively Identify Suspicious Activities In Real Time

Machine learning (ML) algorithms and data analytics detect anomalies or patterns signaling fraudulent behavior. This approach relies on diverse data sets and model retaining to keep up with evolving fraud trends. It involves technologies such as anomaly detection, predictive modeling and behavioral analysis. 

Yet, it’s important to remember that not all applicant or user behaviors are easily categorized as fraud or non-fraud. Since many fraud detection systems treat fraud as a yes or no problem, your operational risk exposure can increase. Your business may dismiss thin-file customers and fail to meet sign-up goals. 

Instead, consider a platform that minimizes fraud losses by considering the anticipated severity of each transaction. Instnt’s risk models and risk transfer mechanisms allow you to onboard qualified applicants and shift potential liabilities off your balance sheet. By continually monitoring transactions and identities, Instnt Verify blocks account takeovers for continued assurance.


Optimize your fraud detection system using:

  1. Natural language processing (NLP) techniques: Train ML algorithms to interpret text and extract information, keywords or sentiments related to fraudulent behavior. It can look at customer service communications, chat logs or emails.
  2. Anomaly detection: This approach uses a baseline for normal behavior to identify deviations and flag suspicious activities. It assesses network traffic, transactional data and user behavior.
  3. Network analysis: Analyze networks, including data flow, behavior anomalies or communication patterns, to find indicators of malicious activities. This method can identify unauthorized access attempts or abnormal network connections.
  4. Ensemble methods: Combining multiple ML models reduce false positives and improve accuracy. A recent Journal of Applied Intelligent Systems & Information Sciences study found "that almost all ensemble methods perform well on fraud data."
  5. Behavioral analysis: This ML algorithm compares real-time behavior against models or profiles of a typical user. It can alert institutions if it detects variations from the usual behavior pattern.
  6. Predictive modeling: Models use historical data to estimate the probability of an event being fraudulent. It looks at several variables and their relationships and assigns a risk score to activities or transactions.

Leverage Technologies to Detect and Prevent Fraud

The bottom line is that  83% of global consumers told Experian that "security is the most important factor of online experience." Therefore, your fraud detection and prevention systems must work seamlessly from the second new users apply and create an account. 

Instnt Accept™ is your first line of defense. This fully-managed acceptance platform provides collective intelligence fraud prevention and fully automated verification, including screening solutions required for your industry. Instnt Accept drives top-line revenue by increasing signups and providing up to $100 million in fraud loss liability protection.

Along with decreasing operational risk exposure, Instnt helps your organization comply with Basel III requirements for regulatory and economic capital. Instnt Accept works with Instnt Access, a portable Know Your Customer (KYC) application. Using Web3 decentralized identity standards and the Hyperledger blockchain, Instnt lets customers access accounts with one click without sacrificing security. 

For continuous identity assurance, Instnt Verify validates transactions from authorized account owners. It offers device and behavioral intelligence, preventing transaction and account takeover fraud.

These solutions use predictive fraud models, such as:

First-party fraud model: This ML model uses real-time customer data to catch subtle anomalies in device usage. It may look at velocity metrics or signup behavior, even if the personal information is primarily legitimate.

Third-party fraud model: This ML model looks at real-time customer data that contains mixed or stolen personal information, such as a user that provides a valid email address with another person's phone number.

Synthetic fraud model: This ML model aims to detect real-time transactions made with fake information, like made-up addresses or names. 

Integrate External Data Sources to Improve Fraud Detection 

External data sources can confirm and validate customer identities, helping reduce account takeovers and identity theft. For example, with Instnt, you can obtain documents like driver's licenses and passports during the KYC process. Identity verification services also look at watchlists, public records and credit bureaus to assess an individual's credibility and authenticity.

For optimal fraud detection, consider the following methods for gathering external intelligence: 

  1. Using ML algorithms to analyze historical and real-time information from external data sources to uncover patterns or trends
  2. Assessing risk by incorporating data about previous legal actions, fraud incidents and financial history into the fraud detection system
  3. Considering external data sources like geolocation data and internet protocol (IP) intelligence to identify usage from high-risk regions or fraudulent access attempts

Discover the Solution for Reducing Exposure and Fraud Risk

Fraud risk management is essential for your organization. Yet, emerging threats like mobile malware jeopardizes fraud detection and prevention processes. Machine learning algorithms and models that leverage historical and real-time data can reduce risk, especially when used throughout the customer lifecycle. 

With Instnt, you achieve identity assurance from account opening on Day-zero to transactions on Day-N. Its predictive fraud models consider real-time data, including device and behavioral information. Instnt balances fraud risk with your organization’s appetite for growth. Its risk models and loss-liability insurance allow you to accept more applicants without increasing your liability. Learn how to optimize fraud detection by scheduling an Instnt Demo today.


Jessica Elliott is a business technology writer specializing in cloud-hosted and cybersecurity services. Her work appears in U.S. News, and Investopedia.



Experian – 2022 Global Identity and Fraud Report

Journal of Applied Intelligent Systems & Information Sciences – Ensemble Learning for Fraud Detection in E-Commerce Transactions: A Comparative Study




About the Author

Instnt's fraud loss indemnification technology provides coverage of up to $100M for fraud losses stemming from synthetic, third-party, and first-party fraud. With Instnt's comprehensive fraud loss protection, businesses can confidently extend their services to a wider customer base, enabling them to embrace more opportunities and enhance revenue streams while maintaining a secure, fraud-free environment.