Financial companies deal with millions of consumers and posses large amount of personal and transaction data. They loose millions to fraudsters and have deal with strict regulatory requirements. Storing large amount of data securely in a scalable big data platform is essential to deal with compliance, detection of fraud, serving customer in a better way and marketing new products & services.
Financial companies screen applications daily for loans, credit cards etc. They look at customer's credit history, income and other factors to determine eligibility.
Lot of times fraudsters fake identities or reuse someone else's id to apply. Detection of these fraudulent applications becomes very time consuming and difficult. Big data provides necessary compute and graph based solutions to determine fraud rings and perform it in automated fashion. Automation saves time and cost and reduces fraud.
Financial transactions often go through many systems of different line of business.
Tracking transactions and performing analysis is critical to detect fraudulent money laundering activities. Hadoop provides large amount of data storage and analytics to track transactions and detection of money laundering.
Financial companies collect security tick information from various data systems like Bloomberg terminal etc in real time.
They can leverage Hadoop to store the ticker price history to perform analytics and look for trends. They can also detect real time trends and make decisions.
Financial companies can store customer transactions in Hadoop and build models to set customer's baseline profile for example where customer travels, spends on what categories etc. Seasonality can be added to determine correct pattern and detection of anomalies in real time.
Knowing customers better will drive right targeted campaigns, which will result in better customer experience and conversion. Campaign Management solution allows creation of customer data sets to know who are the customers, where are
they location, what they are looking for etc.
Marketers can apply machine learning models to segments customers using variety of campaign elements.
Using audience segmentation feature marketers can effectively target their customers.
Financial companies are required to follow many regulatory laws. They need to maintain data about their customers, transactions in a secure and easily accessible platform. They also need to file regular reports. Financial companies can leverage Hadoop to store customer and transactions at central place securely. They can build automated pipelines to aggregate large amounts of data fast and generate compliance reports.
Financial companies give loans, credit cards to customers and its necessary for them to determine and monitor chances of a customer's delinquency/credit risk. Financial companies can use Big data to store customer details along with transactions and build scoring models to determine credit risk. They can perform in depth analysis on large number of transactions (purchases, loans, deposits, authorizations, payments) easily on Hadoop.
Financial companies' customer care department connects with customers for variety of reasons like support, billing, new customer acquisitions. They connect using chat, phone calls, email, mobile apps etc. They can store chat logs, emails, audio and call transcripts etc in Hadoop to gain better insights, track customer sentiment, improve customer experience and improve operational efficiency.
Financial companies depend on their customers for business and their success depends on them. It is very important to understand customers, their needs, satisfaction to build long lasting relationship. This drives more business and
creates brand value.
To understand customers it is essential to know:
Big data solutions enable companies to collect customers data from all touch points and allow them to know and serve their customers well.