Banking, financial institutions & customers have been facing fraud for a very long time, in fact ever since the financial industry was created. The chances of fraud being attempted are almost guaranteed wherever money and/or private data are present.
As the use of digitization and use of technology increases, it also increases the ways and means for fraudsters to leverage the same technology to commit fraud.
Fraud detection identifies an actual or expected fraud that has or may take place using advanced technologies like AI, OCR, and ML to identify potential threats, mitigate risks, and prevent their recurrence. Banks, financial institutions & any other organization that deals with money, finance, or any other financial instrument need to implement strict measures, systems, and processes in place to detect fraud at an early stage or if possible before it takes place.
Fraud detection consists of an organization being proactive and reactive and using either manual or automated techniques.
Why is fraud detection important?
- Helps stop customers from being cheated of their hard-earned money.
- Reduces costs and expenses associated with the fraud.
- Reduces exposure to further frauds.
- Helps increase customer, staff, and shareholders’ confidence and trust.
- Helps improve the bottom line and results.
Detection of fraud and financial crimes is one of the most critical and pressing issues for any banking, financial institution, or even consumer.
Types of financial frauds:
- Authorized push payment – Fraud is committed by tricking the payer into authorizing a payment to a fraudulent payee or receiver. The payee or the receiver poses as a legitimate business or individual and can use phishing or other methods to obtain payment authorization.
- Account takeover – A fraud like this is committed when a hacker or a criminal can gain access to someone’s banking account and can take control to make unauthorized transactions. This type of fraud is committed through phishing, malware, or when a user clicks on unknown links in text messages or an email.
- Phishing – Use of fraudulent emails or text messages which leads a victim into revealing their personal and financial information which is accessed by the fraudsters.
- Identity theft – Phishing can result in identity theft as personal and/or financial information is obtained by the fraudster and this information like banking PIN, social security number, etc are used to steal money.
- Investment fraud – The victim is convinced to put money into false schemes and the money gets transferred to the fraudster directly.
The benefits of using AI to assist and enhance fraud detection in real-time are:
- Identifying hidden patterns – Analytics using AI assists in identifying patterns, and trends of potential fraud and generating scenarios for such an event. Such analytics enables banks to take appropriate steps to close the loopholes and take pre-emptive steps to prevent fraud.
- Data integration – Data streams from various branches and terminals are pooled. AI and data analytics are used to integrate and process this data to find patterns and trends of potential fraud or to find the source of any fraud that has already taken place.
- Harnessing unstructured data – Data streams from various sources and branches are stored in data warehouses. Using AI and data analytics, this unstructured data is converted into usable structured data which can be analyzed for potential frauds or activities.
- Increasing accuracy – AI enables the computation of vast volumes of data accurately and gives actionable insights empowering the banking teams to work more efficiently and also check for fraudulent activity. The analytics helps in differentiating between genuine and fraudulent customers and activities.
In conclusion, Artificial intelligence and machine learning are helping find efficient solutions for the detection of fraud in the banking system. Banks generate huge volumes of data and this data bank can be used to train machine learning models which can flag fraudulent transactions. Fraud detection using machine learning is also known as anomaly detection, also known as outlier detection which uses the data to create a graph. Transactions that fall out of a specified range are flagged as anomalies or outliers, which can be fraudulent transactions. As an example, when a customer makes an ATM transaction or makes a purchase, AI and ML map the transaction to a specific physical address where the ATM is located or where the purchase transaction is done including the IP address of a mobile device or a computer. If the same debit card or account is used for a transaction from another location or IP address, details like transaction id, source IP, start-end time, and destination port are generated and the transaction is flagged and marked as an outlier or an anomaly.
Banks and other financial institutions are increasingly making use of technology and digitization. Loopholes and general unawareness of customers and users are being increasingly used for fraud. The benefits of AI in finding and preventing banking frauds in real time are many, and bank fraud prevention software is a crucial piece of armor for banks to use.