Big Data needs no introduction in the finance industry. But real-time is the new entry that is sweeping the finance world off its feet by becoming more viable for the masses. The waves have begun to show.
While a lot of financial institutions are updating their data in real-time, it is not the same as analysing it in real-time. The perks of incorporating real-time analytics or even near real-time analytics offer a constant stream of data, allowing for instantaneous actions in response to momentous events.
Think about better decision making in terms of marketing decisions, loan extension, fraud prevention, increasing customer loyalty, risk management, and the list is just endless.
To minimise latency, real-time analytics is made to operate at the very edge of the network. In simpler terms, raw data gets analysed even before it makes it to the data warehouse, providing swift results.
Continuous streaming is best-suited for services and sectors that cannot afford to experience any downtime.
The four essential components in the process of implementing real-time analytics software are:
The Broker helps in managing data’s availability and its consumption
The real-time application analytics is executed via Stream Processor by sending and receiving data streams
Analytics Engine is the logic centre of real-time analytics. It is here that data streams are blended and values are correlated.
It gathers real-time data streams from various data sources.
There was a time when the data that was collected was processed and analysed but in batches. This means that by the time that specific set of data got its turn at being turned from raw to useful, it had already gotten old and in most cases, had lost its true value and potential. With the right utilization of Data Science, it is now possible to gather real-time data and run analytics in real-time as well.
'Decisions made on historical data are hardly valuable'
Real-time data brings along the following benefits:
Understanding what’s going on inside your customer’s head in real-time is the key to offering successful financial investment services.
With machine learning, you can study the activity of your customer in real-time. The real-time data processing is grounded on various big data technologies including Apache Kafka, MongoDB and NoSQL. This aids in handling the humongous amount of customer behaviour data.
Decision making is based on transactional data gathered through a customer’s browsing history.
For instance, a client with a portfolio is logging into their account repeatedly and looking at a specific stock each time. Since you now have their activity details in real-time, the client can be suggested to make the purchase and expand their portfolio.
Real-time data gives us a bigger picture to help make better long-term decisions.
Marketing is a huge foray and to be done correctly, you need to get a lot of why’s, how’s, where’s, and when’s correctly.
Real-time analytics can help you with each of these questions and then some. With the modern DMP or Data Management Platform, data is being constantly collected, ingested and analysed and that too from various sources. This gives you insights about who to target, the best time to target them and what platform to utilise for the purpose.
For instance, your company is launching a new loan scheme and wishes to target the existing women bank account users. Instead of a generic campaign, you can now identify who, when and how to approach from the existing set.
With real-time analytics, it is possible to get the information instantly if the user has put in a request for withdrawal for a high amount or if someone is checking out the loan page for quite some time now. These sets of users can be targeted for new loan schemes via specific emailers, websites, app notifications, etc. Thus, you cater to their immediate requirement of cash.
The customer’s intent and ability to pay can now be analysed in real-time and this is making all the difference in the world.
You can even customize the offer. Customer segregation is more productive and sophisticated than ever and it is all thanks to real-time analytics.
For instance, if the borrower has a stable income and an excellent credit history, then a low-interest loan can be extended. On the contrary, someone with a poor credit score/ history can be offered the same loan at a higher interest rate.
The scoring system is a rich example of real-time analytics that has helped banks in deciding on an instant loan extension. The creditworthiness of a person is analysed on the basis of a scoring system that ranges from 300 to 850.
The higher the number is, the better the person’s credit score is.
In this ultra-competitive world, it is integral to know what your customers want and when they want it. At the same time, doing it all now is essential and real-time analytics sure has changed the way things are being done.
Uncovering crucial information at the right time is the key to the kingdom. Geotargeting and geofencing are integral points of success with real-time analytics.
But real-time analytics is not the only aspect of data analytics that is taking the finance field by storm. Stay tuned to know more about the other aspects of data analytics that are truly changing the financial sector.