Big Data improves the accuracy in detecting suspicious transactions. Banks have extended POC and pre-implementation phase. The algorithm will detect anomalies in transaction behavior by accessing different features such as products, customers, and risks.

360-degree view of each customer based on how everyone individually uses mobile or online banking, branch banking or other channels.The bank predicts the needs of their customers and understand them better. The team analyzed large volumes of data to identify their customer’s preferred means of communication, such as phone, email, or social media. This valuable information has increased the hit rate of their marketing campaigns four times.

Mostly, a call centre agent facilitates the customer’s request. However, the agents have few ways to determine whether the person they are speaking to on the phone is the actual customer, and this poses a serious threat to that customer’s information. Big data analysis helps to detect these fraudulent phone calls which can help a person identify information like the caller’s location. It is integrated with customer service offices, and the banking agents get alerts if the call is fraudulent so that they can pass the call to fraud specialists.

Offloading data processing workloads onto Hadoop to improve performance and reduce cycle times. Offloading high volume storage and processing onto Hadoop. Delivering ready-to-consume results to traditional data store. Big data warehouses are built on Hadoop and enable data consumption. Data is archived on Hadoop to reduce storage cost and meet the compliances around online data access.

“Market surveillance depends on algorithms to identify patterns in trading data that might indicate manipulation and alert staff to investigate.
But the huge volumes of data can cause an enormous number of alerts, many of which are false alarms.To tackle this issue, a machine learning software is developed that can look beyond the patterns and understand which situations truly deserve to be mentioned as red flags. In other words, the machine learning software will learn which trading patterns lead to legal charges, to classify the right ones. Also,moving market surveillance system to cloud, gives it more computing power to analyze data quickly. ”

Banking and financial services need to do regular compliance and audit for their data, finance, etc. They come under regulatory body which requires data privacy, security, etc. Big data analysis can help in analyzing the data and finding the situation where financial crisis or security issue can occur. This will help the banks and financial sector to save from any compliance and regulatory issues.

Customer behavior data points may include spending habits, geolocation, and recurring payments such as gym memberships or online services. In order to have a fully-functioning predictive analytics application for discerning and analyzing customer behavior, a bank must use their customer data to train a machine learning model. Predictive analytics can also be used in credit scoring applications for client banks and enterprise creditors to more accurately estimate the risk associated with a potential customer. Banks use trading insight found using prescriptive analytics to help their clients who buy and sell stocks make more informed decisions.

Intelligent Receivables (IR) is a well-suited solution for firms that manage lots of payments where the remittance information is either missing or received separately from the payment.