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# How The Financial Services Industry Should Use Big Data To Regain Trust
- URL: https://www.thedigitalspeaker.com/how-the-financial-services-industry-should-use-big-data-to-regain-trust/
- Published: 2013-12-11T00:00:00.000Z
- Updated: 2026-08-04T05:42:56.000Z
- Description: If there is any industry that can benefit from big data, it is the financial services industry, but they will have to deal with many privacy issues.
- Author: Dr Mark van Rijmenam, CSP
- Tags: Big Data

If there is any industry that can benefit from [big data](https://www.thedigitalspeaker.com/big-data-speaker/), it is the financial services industry. Of course the first use case that comes to mind is the ability to reduce risks, whether it be credit risk, default risk or liquidity risk. The industry that has lost the trust of its customers a lot since the crisis of 2008 can turn to big data to understand customers better and improve customer satisfaction by developing those products that their customers are looking for. But there are of course more possibilities.

## 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](https://www.thedigitalspeaker.com/ai-insurance-speaker/) company [Insurethebox](http://www.insurethebox.com/?ref=thedigitalspeaker.com) is a [pioneer](http://www.ibmbigdatahub.com/blog/driving-down-price-risk?ref=thedigitalspeaker.com) 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](http://blogs.wsj.com/cio/2013/03/11/visa-says-big-data-identifies-billions-of-dollars-in-fraud/?ref=thedigitalspeaker.com) a system that is capable of analysing 500 aspects of a transaction at once. With an annual [incremental](http://blogs.wsj.com/cio/2013/03/11/visa-says-big-data-identifies-billions-of-dollars-in-fraud/?ref=thedigitalspeaker.com) 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](https://www.thedigitalspeaker.com/ai-finance-speaker/) 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](http://www.dutchnews.nl/news/archives/2013/05/direct%5Fdebit%5Fprocessor%5Fplans%5Ft.php?ref=thedigitalspeaker.com) [Equens](http://nl.wikipedia.org/wiki/Equens?ref=thedigitalspeaker.com) (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.

##### *Image Credit:* [*JHK2303/shuttestock*](https://www.shutterstock.com/g/JHK2303?ref=thedigitalspeaker.com)

## Frequently asked questions

### How can big data help reduce credit and fraud risk in banking?

Big data allows financial firms to build individual risk profiles using purchasing behaviour, social network activity, lifestyle, and public data sets, improving credit default predictions. It also enables outlier detection to spot fraud, such as when a credit card is suddenly used in distant locations within a short timeframe, and even allows transactions to be analysed and blocked in real time before completion, as Visa does by analysing 500 aspects of a transaction at once.

[Link to this question](#faq-how-can-big-data-help-reduce-credit-and-fraud-risk-in)

### What happened when Equens tried to sell transaction data?

Equens, the largest pan-European payment processor, decided to sell transaction data but faced a lot of negative reactions from consumers who were sensitive about how their personal financial information was being used. As a result, Equens had to withdraw its plan, illustrating how cautious banks must be when monetising customer data.

[Link to this question](#faq-what-happened-when-equens-tried-to-sell-transaction-data)

### How can financial firms use big data to improve customer satisfaction?

Firms can analyse product usage, such as how a mobile banking app is used based on location, time, clicks, and navigation, to gain instant feedback without lengthy surveys. Social media sentiment analysis reveals real-time customer opinions and key influencers. Faster online tools, added services like instant account search, and aligned internal systems so representatives already know customer details also help improve satisfaction and rebuild trust.

[Link to this question](#faq-how-can-financial-firms-use-big-data-to-improve-customer)

### Can big data help banks prevent physical robberies?

Yes, big data enables banks to understand which ATMs are most likely to be targeted by criminals and how often, based on geographical location and other data sets. This allows banks to take appropriate measures to reduce robbery risks or install smart cameras capable of detecting criminal activity before it happens.

[Link to this question](#faq-can-big-data-help-banks-prevent-physical-robberies)