The finance industry is a highly competitive space. It faces a new generation of disrupting banks and regulations. It’s an industry that needs to utilise big data to drive personalisation, security and fuel everyday investment decisions.
The financial services industry has always been at the forefront of technical innovation. The availability of new datasets has provided a powerful way to understand behaviour and offers new directions for the financial industry to be predictive.
Big data application in financial services goes beyond predicting share prices. New data types are revolutionising the space.
We’re going to break down some examples of how big data is being used in financial services.
Personalisation has long been a priority when dealing with consumers. This is true both inside the finance industry and across other verticals with a strong consumer presence.
Disruptor banks have begun to establish themselves in the finance sector. One thing that these banks have done well is personalisation.
By understanding users spending habits and behaviour, they can offer more personal spending products and recommendations.
For example, if a bank had the right data sets around its customers, it could provide services that truly bring value to the end customer. If a bank understands what their customers spend, where they go and where they work they can, for example, suggest that a travel card could save them a lot of money each month.
It’s in these areas that financial services can learn from disruptor banks. With the right dataset, the banking experience can be personalised for customers. This will allow financial services to boost customer loyalty and drive cross-selling of their products much more effectively.
There are few industries where security and fraud are more of a threat than in the financial services.
As technology has advanced those in the space must be smarter and better adapted to the fast-changing tactics of those looking for weak points in sometimes outdated systems.
Adding location intelligence to the equation adds an extra layer of security for customers and allows financial institutions to instantly provide checks based on where a customer uses its products.
Location offers a new way for security teams to identify fraud. Location data can help to educate security systems on customer behaviour and can form a strong base from which to detect irregularities in financial behaviour.
Financial services continually process an incredible number of transactions. A location overlay which includes operational rules can help to determine when to flag records as fraudulent.
These processes enable financial services to provide better safeguards to their consumers and clients.
The rise of big data carries enormous potential for investors. Many have implemented predictive systems that are designed to understand data sets, digest vast amounts of data and then inform investment decisions.
These data sets have proved successful, but accurate location data is rarely used to optimal effect in this area. Location intelligence provides a more detailed understanding of trends and ingesting these precise datasets can help investors to stay ahead of the competition.
Understanding how populations move and behave en mass is readily available in the online world. Location offers something slightly different. It helps to understand how consumers move in the offline world.
Using location allows minimal lag between an aggregated understanding of consumer trends and investors being able to forecast the performance of portfolios and financial markets.
This allows investors to act quickly and decisively. Location is a fantastic indicator of market, brand or individual behaviour. With the progress made in accuracy by some in the space, this will prove to be one of the next steps in predictive analytics for the financial sector.
For insurance location underlies everything. Many insurers can immediately benefit from quality location data to better model risk and dramatically improve their underwriting and pricing.
In insurance, nearly every data point has a relation to location.
Crime data – understanding crime risk in specific locations including historical data and predictive solutions can provide significant advantages to insurers.
Catastrophe – using data analytics insurers can mitigate the risks involved with catastrophes based on a customer’s location.
Behavioural data – using new kinds of behavioural data, such as location, can allow insurers to understand consumer behaviour better. This helps to predict risk and assist with pricing.
These datasets are now available in large quantities. Precise location allows for more accurate insights and the ability to generate this data, as well as store and process it, has changed the game for the insurance industry.
For financial businesses, consumer data also holds the key to new revenue. It allows companies to maximise revenue from existing channels.
For example, using precision data marketing financial companies can identify products and services that are a much closer match for specific customers. This provides more value for both customers and branded partners.
Understanding customers behaviour allows companies to understand which type of person is more valuable for their business.
Using this data, it’s possible to build lookalikes and use this to drive more targeted marketing activity to customers that represent a greater potential for higher revenue.