These are times of increasing competitiveness. Fintech companies launch new products and services at high speed, always focusing on a digital consumer, who requires immediacy and is extremely connected and informed. In this context, financial institutions – including traditional banks and digital institutions – need to become increasingly agile and, at the same time, keep their risks under control.

The answer to this double challenge is big data. The organizations in the financial sector have enormous volumes of data -as well as access to external sources- that, combined and analyzed, allow to know in depth each one of their clients: from their behavior as consumers, to their savings models and investment; and from their preferences for a certain style of services, to their participation in frauds.

At the lowest risk

One of the most frequent uses of big data in this kind of industry is risk management: nothing less than one of the business priorities. Here, the risk assessment of credit profiles stands out. Today, in seconds, a bank can know how compliant the customer who asks for a loan is. In other words, it is possible to grant it almost immediately with much lower risk than just a few years ago.

In this sense, it is not only a benefit for financial companies, but also for society: difficulties in accessing credit are one of the great obstacles to financial inclusion, since people are marginalized with no credit history in a kind of vicious circle where they can never get their first loan or, if they do, it is at very high interest rates. Big data breaks this model, since it allows evaluating risk based on other digital sources.

In addition, the application of big data in fraud management generates millions of dollars in annual savings and avoids numerous legal problems. It is also combined with predictive analysis tools that evaluate and correlate fraudulent behavior and detect suspicious patterns and activities.

Always in order

Compliance is another of the great challenges that financial institutions experience: different regulations from country to country -and even from district to district-, frequent changes in the game’s rules, supervision of different organizations…

When a bank or financial institution fails to comply, it is subjected to multiple problems ranging from fines and loss of reputation to temporary closure of operations.

With big data, it is possible to keep up with regulations and even motivate proactive work so that the entire organization is always aligned with the latest version of each current regulation.

On the stock market

Another case is the so-called high-frequency trading: algorithms that combine big data and artificial intelligence to evaluate multiple stock market sources, process millions of operations in fractions of a second and aim to maximize income on the sale of shares from even very small profits per transaction.

This same model is being used to offer self-assisted investment advisory services: millions of alternatives are evaluated in real time and those that are always performing the best are suggested.

Grow the business

Beyond all the above, the most interesting potential that big data shows for the financial services industry, is the launch of new products and services that are totally personalized, considering the client’s preferences, their relationship with the company, the type of queries, interactions with the different contact points in the channels, searches…

It is even possible to arrive with specific products at moments almost in real time: if it is detected, for example, that someone is looking to change their car, their credit profile can be evaluated to offer a comfortable and accurate loan so they you can meet their objective. The bank will not only generate this operation but will also significantly increase the level of commitment.

The quote “time is money” needs to be adapted and adjusted to this digital age. Today, the expression seems to tend towards “data is money”. Banks and financial institutions already have the data. They just need to apply the correct strategies, supported by technologies to cover the second part of the sentence.