To reduce the risk companies are facing
For each customer an individual risk profile can be created based on many different variables such as past purchasing behaviour, online and offline social network activities, way of life, public data sets etc. The more data that is used, the better the risk profile can be determined, thereby decreasing credit default risks. The insurance company Insurethebox is a pioneer in using big data to reduce risk. Customers implement a device in their car that measures exactly how, when, where each insured vehicle is driven. Based on this information an algorithm determines the driving behaviour (including acceleration and deceleration behaviour among others) and a corresponding risk profile. Each customer receives as such a tailor-made insurance offer. The better a customers drives, the better the offer they receive.
Big data technologies also improve enterprise risk management. It is possible to add different data sets and use them in determining the risk profile of a client when requesting for a loan. Factors such as claims, new business, investment management factors or lifestyle of the managers will provide a better picture regarding the risk appetite of an organisation than a business plan that is based on many different and unknown future variables. This will result in achieving more sophisticated and accurate predictive models that will reduce enterprise risks while companies will be helped better.
Fraud can be also easily detected with big data when through data analyses it is seen that suddenly a customer deviates from a standard pattern that a customer had for many years. Outlier detection is a powerful tool to discover anomalies. Algorithms can easily detect instantly when suddenly a credit card is used in distant locations within a short timeframe. This will indicate possible fraud detection. Even better will be to be able to analyse a transaction based on different data sets while the transaction takes place, allowing organisations to block a transaction before it has taken place, instead of checking the transaction afterwards. Visa has implemented a system that is capable of analysing 500 aspects of a transaction at once. With an annual incremental fraud opportunity of $ 2 billion, Visa has all the reasons to pay a lot of attention to big data.
Big data can also stop criminals who use the ‘old-fashioned’ way of robbing a bank. Big data enables banks to understand which ATMs are the most likely to be targeted by criminals and how often based on their geographical location and many other data sets. Banks can then take appropriate measurements to reduce the risks of a robbery or install smart cameras that can detect criminal activities before they happen.
To regain trust of customers and improve customer satisfaction
Analysing the usage of the many products that financial services firms have explains a lot about the behaviour of the customers. Although banks do not do this, or at least they say they do not do it, they have the possibility to understand customers better than customers understand themselves. The payment information explains a lot about customers. For that reason, when payment provider Equens (the largest pan-European payment processor) decided to sell the transaction data lot of negative reactions appeared and Equens had to withdraw their plan. Customer satisfaction can be improved in many different ways with big data, but banks should be careful how they should approach this.
Online tools can be improved faster, additional services can be added (such as instant search results when entering a bank account number) and a customer can feel more appreciated if he or she calls and the representative know all details because all internal systems are aligned and connected with each other.
Using social media algorithms it is possible to understand the sentiment of customers in real-time, knowing how they think about or use new products, services or commercials. In addition, it can be used to find the most important influencers and how they think about the products or services. Analyzing how products are used can give insights in how the products or services need to be improved. For example, when a bank analyses how a mobile banking application is used based on location, time of day, where people click, how they move through the app, how long they use the app or search for items within the app can all give indications for improvement. Instead of asking the customers through long and expensive feedback surveys, the feedback is instant and without bothering the customer.
To increase sales and reduce costs
We humans live a pretty predictive life and as so many products are bought with a debit or credit card, it is possible to find patterns in consumer behaviour based on where and for how much and what they use their debit or credit card. When this behaviour is monitored, financial services organisations can take predictive action based on future events and sell for example additional products at the right time to the right customer, thereby increasing the conversion rate (for example when someone suddenly buys more groceries because a couple moved in together).
The financial services industry is also known for the large amounts of legacy systems that cost a lot of money to maintain. With a big data platform it is possible to migrate the legacy data to the new platforms, while in turn being able to add valuable data to the analysis that will deliver new insights, which could lead to new revenue opportunities or a reduction of operational costs. Operational efficiencies can further be improved when transactional and unstructured data such as voice recognition, social comments or emails are monitored and analysed to anticipate on future workload and change the staffing needs accordingly in call centres or branches. In addition, when all customer contact points are collected and shown via one platform, staff will be able to help customers faster and better, which could also lead to a reduction in customer churn
The possibilities for the financial services industry are almost endless, but they will have to deal with many privacy issues when going full steam ahead with big data. As the example of Equens in The Netherlands showed, consumers are very sensitive regarding personal financial information that is used to make more money by banks that have lost so much trust in the past years. Therefore, even more than for the other industries, the four ethical guidelines are extremely important to adhere to.
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(regaining customer trust through big data / shutterstock)