Financial organisations are facing rising fraud instances impacting their bottom line. Several reports find that these organisations lose 5% of their annual revenue on average to fraud. Organisations must realise that the frauds are not impacting their revenue alone.

Instead, fraud reduces consumer confidence in the business. For instance, if a bank fails to detect fraud, consumers will likely stop trusting the bank. They may start thinking about changing their bank for one that offers better protection. Frauds can reduce worker productivity.

How?

Frauds would compel them to dedicate their energy to detecting and preventing them. Over time, they would lose focus of their main responsibilities, which can hamper organisational operations. The entire team suffers if one employee is not performing their role effectively.

However, there are several ways through which businesses can reduce fraud. AI and other automation technologies, such as robotic process automation (RPA), are helpful in fraud detection. This article will review the use of AI and similar technologies in fraud detection.

Automation Technologies & Fraud Detection

1. Purchase Profiles

AI can help build purchase profiles for each customer. For example, a customer eats out each weekend and tops up their car each Monday. Over time, AI will build a unique profile for this customer. The system will flag the transaction as suspicious if there is anything out of the ordinary, such as eating out on a weekday or topping up gas on another day except Monday. With each transaction, the user’s profile will be updated.

2. Know Your Customer (KYC)

AI and RPA can match KYC documents with the bank’s database in real time without error, ensuring that the authorised user is making the transaction. Employees are prone to error, and their negligence can result in a fraudulent transaction passing through the security checks.

3. Fraud Score

AI and RPA can help approve or reject a transaction based on the fraud score. The fraud score assesses the fraud risk by taking into account different variables. For instance, the user’s IP address, transaction amount, place of transaction, purchase address, or frequency of card usage can help determine the fraud risk. If a user only shops from a specific area and the new transaction is from a completely different area, the AI system will mark it as suspicious.

4. Speed Data Analysis

RPA automates the data collection process and ensures the relevant data is available in a standard format. Organisations can then train AI models to analyse the data, thereby speeding up their fraud investigations.

5. Reduce Human Error

How does a fraud happen? Fraudsters exploit an organisation’s weakest link which are the employees. Human negligence is responsible for fraud. Automation technologies remove this link by limiting human interaction with the systems. With advanced systems in place, defrauding the organisation is not easy.

Conclusion

Consumers expect increasing levels of digitalisation from their financial institutions. After all, greater digitalisation means further ease for the customer. However, this digitalisation must not come at the expense of the customer’s safety. If an individual perceives that their financial institution is compromising their safety, they will not hesitate to go to a competitor.

Therefore, financial institutions must focus on automating their systems for greater fraud detection and prevention. Agile Managex Technologies can create custom RPA bots for your financial organisation. These bots will automate critical processes, reduce human error and improve your organisation’s fraud detection systems. Contact us for further information.

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