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What is Fraud Detection?

Fraud detection is a process or a set of activities undertaken to detect and block the attempt of fraudsters from obtaining money or confidential data through false means. Fraud detection analytics solutions today are jointly integrated with businesses and go beyond detection to prevention in real-time. They are widespread across the banking, insurance, government, medical, and public sectors. Fraudulent activities include money laundering, false bank claims, cyberattacks, identity theft, forged bank checks, and many such illegal practices. Therefore, organizations that implement advanced fraud detection and prevention technologies are integrated with AI-based artificial intelligence (AI), and risk management strategies to tackle growing fraudulent transactions across varied touchpoints. These techniques apply adaptable and predictive analytics (i.e., machine learning) to create a fraud risk score along with real-time monitoring of fraudulent events or crimes.

Fraud detection techniques generally involve data analysis-based techniques. These techniques are mainly categorized as statistical data analysis and artificial intelligence or AI-based techniques. Statistical data analysis techniques include statistical parameter calculation, regression analysis, probability distributions & models, and data matching. AI-based techniques are data mining – classifies, segments, and groups data to search through millions of transactions to find patterns and detect fraud. Neural networks learn from suspicious-looking patterns and use those patterns to detect further.

With machine learning algorithms, it can learn from historical patterns and recognize them in future transactions.

For pattern recognition, the algorithms detect approximate classes, clusters, or patterns of suspicious behavior, either automatically or manually.

Some of the fraud detection tools are Fraud Scoring, 3-D Secure, Machine Learning (ML), Fraud Blacklists, Biometrics, Velocity Checks, Address Verification, Device Fingerprinting, Proxy Piercing, and Geo location.

How Fraud detection works in Banking & How AI is used in Banking to detect fraud?

Fraud detection in banking is a critical activity that can increase a series of fraud schemes and fraudulent activities lead to high risks and consequences to banks and customers alike, both in terms of credibility, financial losses, and trust. There are a few of the most common types of banking fraud like credit card fraud, money laundering fraud, mortgage fraud, cash transactions, tampering with invoices and other documentation fraud etc. Mainly, most banks employ rule-based systems with manual consideration for detecting fraud. While these systems were doing a pretty decent job but in recent years have become more inconsistent. That’s because new fraud patterns are developing rapidly, and these systems are not able to integrate which allows frauds to go undetected, resulting in huge revenue losses.

Banks are finding that AI for fraud detection is fast, efficient, and effective. Financial institutions are deploying AI-based systems and AI applications to help prevent fraud risk. Fraud Risk is the risk of unexpected financial, reputational, and material loss as the result of fraudulent actions of persons external and internal to the organizations. The financial institution’s trusting AI can now get ahead of fraud before it takes place. There are several applications of AI for fraud detection in the financial services industry. Analyzing transactions is one of the basic functions. From risk scoring to grouping consumers into profiles or identifiable clusters, each application is important in building a robust fraud detection strategy.

Banks are using AI for fraud detection and building purchase profiles to accurately detect fraud. Financial institutions and banks must first understand typical customer behavior. By using machine learning techniques to sort through vast amounts of data from previous financial and non-financial transactions. Banks can build and place customers into several different profiles. Profiles are useful as they provide an up-to-date image of activity on an account and help make predictions on future behavior.

Developing fraud scores all transactions are assigned a fraud score by using data from past legitimate transactions. Incidences of fraud and risk parameters take into account variables such as transaction amount, card use frequency, time, IP address of purchase, etc. This is used to assess the fraud risk involved with that particular transaction. Fraud scores are used to automatically approve a transaction, flag it for review, or reject it altogether. Using machine learning, the accuracy of fraud scores improves over a while as the machine is in learning mode.

Fraud investigation: Machine learning algorithms can analyze hundreds and thousands of transactions per second. Neural networks take this capability by making decisions in real-time. These technologies are successful in selecting the unmanageable number of flagged transactions that occur and providing a brief list of those that require further investigation by a human counterpart.

Investigating and prosecuting fraud claims can take more time, so ensuring agents are provided with the proper tools to increase efficiency is essential. This application of augmented intelligence can help agents and teams prioritize and streamline investigations. Know Your Customer (KYC) by AI-backed KYC measures can verify documentation and ID, match fingerprints and even perform facial recognition in clicks. This powerful tool gives the right balance between customer security and convenience.


Fraud monitoring is the core of an advanced fraud prevention strategy. Fraud monitoring is significant to keep track of fraud detection issues like suspicious behavior and identity theft, digital security, and the challenges to keep transactions secure for merchants, customers, and financial institutions. Online fraud can happen at any stage of the user journey, not just at checkout. To reduce the number of fraud incidents, monitoring and early detection of suspicious activities is a must.

Fraud monitoring is very important and planning a fraud prevention strategy going forward is imperative in today’s digital millennium. Constant fraud monitoring is the process of constantly monitoring all actions on a customer’s bank account to detect suspicious transactions or anomalies. From the initial stage of login to other following financial transactions such as payments and funds transfers, monitoring looks at all events and actions of the customer, whether they are monetary or non-monetary to secure the process of fraud detection and identity theft protection. By analyzing and collecting data, fraud monitoring can detect things like changes to an account owner’s profile, adding a payee or a beneficiary, device registrations, and other actions that can create fraud alerts.

Fraud Monitoring is a vital part of a financial institution’s online fraud detection and prevention program. Fraud detection involves ongoing monitoring in two key areas. When fraud teams consisting of analysts, managers, and other professionals involved with fraud detection usually use two common terms to describe how they fight cybercrime and protect customers from identity theft are as follows:

Continuous transaction monitoring: Using analytical resources for continuous transaction monitoring to look at all user actions to detect risks monetary and non-monetary, sensitive and non-sensitive, and monitor every step from the time of login to all the transactions. As part of this constant monitoring, the anti-fraud system looks at events and actions such as making changes to personal information like adding a new beneficiary or payee, an account owner’s profile, and registering with a new device.

The fraud prevention system then analyzes all the data related to the attempt and the outcome, whether it was successful or failed. This creates a historical profile for each customer action before, during, and after the action takes place. This generates data with a detailed historical profile that helps the system to identify anomalies that alert financial institutions that fraud may occur at that very moment.

Continuous session monitoring: is very similar, but the monitoring only applies to the banking session. It analyzes all actions & events across all channels and devices to identify potential risks. For example, if the banking session was initiated on a desktop but authentication was made with a mobile device then the bank can help prevent fraud by forcing the authentication from the same device that is used for initiating the bank session.

What is Fraud Prevention?

Fraud prevention is the implementation of a strategy or an approach to detect fraudulent transactions and prevent them from causing financial and reputational damage to the customer and financial institutions. A strong fraud prevention strategy has become very important as financial institutions are digitized due to the online and mobile banking channels which are more popular. Fraud prevention and cybercrime are inter-connected and always changing in nature.

As fraud prevention professionals develop fraud detection solutions and new authentication, the fraudsters are today using sophisticated strategies to succeed in their fraudulent activities. Though fraud prevention technology has made significant advances and continues to do so, it is very essential to be aware of fraudulent tactics and understand how to prevent fraud.

The main goal of fraud prevention is to reduce the risk of future fraud. Common fraud activities are Denial of Service, Malware, Phishing, Ransomware, and Cyberthreat.

Organizations benefit from fraud prevention by building omnichannel security with FutureAnalytica’s no-code models. By this, the organizations will know the right users have access to the authenticated data under safe conditions.

Enterprises are vulnerable to fraud as it grows digitally with ever-increasing cyber attacks taking advantage of the enterprises. It is estimated cybercriminals steal 30 billion personal data records and cause USD 5 trillion in damages annually.

At we predict your fraudulent transactions in clicks, our inbuilt predictive analytics app helps you speculate which transaction is likely to be fraudulent. We help simplify your fraud prevention efforts and establish digital identity trust that provides seamless, frictionless authentication and gives continuous compliance for the user’s journey.

Protect your organization from phishing attacks and examine deep security analytics to gain actionable insights into threats. Prepare and alert your teams to enhance security measures against cyberthreats without the need to write codes.

Our no-code cloud-agnostic AI platform saves your money, time and effort leading to 5x ROI. It reduces risk and gets your organization ready to stay ahead of developing regulatory mandates and company audits. Easily integrate seamless and secure user experiences while giving end-users control, and power enabling business growth.

Beyond fraud detection, individual banking account holders can reduce their risk of fraud by following a few best practices.

Use Safe Payments: However, online banking is becoming more popular, and it is still important to use discretion before spending and moving money online. How individuals spend money online incurs various levels of fraud risk. Unsecured websites are vulnerable to fraudsters who can invade the site and access payment information.

Protect personal information: Personal information is very valuable. Discourage your customers who readily disclose it, even with the individual who requests the information claiming to represent a trusted organization. Attackers may use phishing techniques to acquire your customer’s personal information. It could be through a phone call, text, or email from a hacker pretending to represent a trusted organization. They may then ask for sensitive information such as bank account details or to inform the customer of an immediate crisis if not sharing PINs etc. Always encourage your customers to confirm the issue by contacting the bank directly. In addition, there are revealing signs of a phishing email, such as misspelled words, unknown email addresses etc.

Check credit reports regularly: Always encourage your customers to monitor their credit reports throughout the year. Different tech applications and banking services will allow individuals to review their credit scores. These tools will help identify fraudulent purchases or any unauthorized credit information taken that may unexpectedly impact their credit score.

Conduct online searches: An individual is at risk of fraud when he or she is shopping online. Before doing business with an online company, users should always conduct a quick search for online reviews. If a user only finds negative reviews or cannot find any reviews at all, they should rethink the risk of doing business with this company.

Remain skeptical about free trials: Free trials help demonstrate the value of their product and are perfect for customers to ensure they are purchasing the right solution. However, a free trial download could also be a phishing attempt for email information or even to control the user’s device remotely. It is always advised before downloading anything from a website, including a free trial, users should make sure they trust the website.


A fraud management system should be able to automatically observe all transactions and events in real-time to detect and prevent fraudulent activities occurring online or in-store. It should stop unscripted access to sensitive customer and company data. Through a management dashboard, the suspicious transaction or event is flagged and can be manually inspected. It also prevents illegal activities related to purchases, payments, and chargebacks. It is employed to secure mobile, web, and app-based financial transactions. A fraud management system is also used for preventing insurance fraud and money laundering. It will also help guarantee compliance with security and data privacy regulations. It can scan internal and external data, employees, customers, transactions, events, and databases. Deterrence and detection are based upon rules or predictive models.

A fraud management system must increasingly use AI for Predictive Analytics that identifies imitation patterns. AI is also deployed for Customer Analytics to detect deviations from a customer’s usual behavior and gathers information from social media and other resources. These patterns are processed to assist in identifying fraudulent behavior. Soon behavioral biometric capabilities will be combined into fraud detection software. Real-time biometric detection will prevent account takeovers and identity theft.

Benefits of Fraud Management system:

1- Proactively monitor for high-risk events or potentially fraudulent

2- Determine transactional risk factors to determine the legitimacy

3- Detect illegitimate transactional behaviors online

4- Provide alerts and analysis tools for administrators

5- Ensure guarantee compliance with data privacy and security regulations

6- Increased coverage to prevent fraud

7- Preventing Complex Fraud

How FutureAnalytica can help in this journey

FutureAnalytica is the only holistic automated machine-learning, no-code Artificial Intelligence platform. It provides end-to-end seamless data-science functionality with data-lake. AI app-store & world-class data-science support, thus reducing time and effort in your data-science and Artificial Intelligence journey.

We at FutureAnalytica reduce false positives and uncover complex hidden frauds through Artificial Intelligence (AI) by advanced financial monitoring with pre-built advanced analytics big data technology that scales for large data size and gives real-time alerts.

With FutureAnalytica’s advanced financial monitoring which is based on advanced Artificial Intelligence (AI) algorithms that can uncover complex hidden patterns in real-time on large data volumes.


Fraud detection and fraud prevention need to be a top priority for any business or organization. A well-designed and implemented fraud detection management system can significantly reduce the chances of fraud occurrence within an organization or business. Furthermore, timely detection of fraud positively impacts the business by reducing future potential financial losses.

Effective detection techniques such as statistical data analysis and AI serve as a deterrent to potential fraudsters. It has become extremely essential to implement robust fraud detection and fraud prevention programs due to regulatory requirements and compliance demands.

We hope this article was insightful and helped you to understand how organizations are vulnerable to high risks of fraud. Fraud Monitoring is important in any business and can benefit organizations by reducing financial loss with a smooth customer experience. Thank you for showing interest in our blog and if you have any questions related to Fraud Monitoring, Detection and Prevention, Machine Learning, or AI-based platforms, please send us an email at

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