Fraud has always been a major concern for banking and financial institutes. With an increasing number of transactions and multiple channel access for the customers, the number, kinds, and intensity of frauds are also increasing. The brighter side of the multichannel operation is the availability of data from all the sources. Analytics tools to analyze this massive big data can help institutes to take proactive actions against frauds.

Analysis of transactional data provides real-time information regarding anomalies that indicate or can lead to fraud. The incorporation of AI and machine learning algorithms helps detect any type of fraud quickly and initiate timely action. One of the most common types is credit-card fraud. With the help of analytics, the losses from these scams have reduced to a great extent.

Banks and insurance companies use machine learning and clustering algorithms to understand the pattern of transactions. Any unusual pattern can be identified and helps in reducing fraud through online transactions over different platforms.