How does Big Data help with financial risk management?

The amount of data available online increases exponentially by the second. As an example, a current iPhone has the same data processing capacity as NASA did when they put the first man on the moon. The analysis and processing of this amount of data, which is available almost immediately, opens a new range of possibilities for both new and old processes.

The financial sector is one of many that can benefit from Big data, especially as it relates to risk management, due to more powerful predictive models, decreased reaction time, and greater effectiveness.

In addition, Big Data allows the FinTech industry to identify the risks and opportunities of emergent technologies in order to provide efficient and sustainable financial services.

The key to using Big Data in risk management

So, what specifically does Big Data offer to aid with risk management in the financial sector?

In the first place, it allows for the creation of more powerful risk prediction models. This, in addition to the ability to access data and analyze it in almost real time, makes for much faster response times, which helps to prevent problems before they arise or keep damages to a minimum if they have already occurred.

Big Data also allows for more extensive risk coverage by providing a global vision and a broader outlook regarding incidents.

Another added value of Big Data is the cost savings that risk management brings: more automated processes, more precise predictive systems, and less risk of failure.

The five V’s of Big Data

There are three features of Big Data that add value to various industries: volume of data processed, velocity of data processing, and variety of data, i.e. the ability to compile data that is both structured (such as databases) and unstructured (interactions on social networks, device configurations, etc.).

In the financial sector, there are two more factors: value and veracity of the data.

In what areas does Big Data help?

There are many different areas to which Big Data can add value regarding financial risk management, although it does so slightly differently in each case.

Fraud Management

Traditionally, fraud management has been tracked manually. However, scammers are using more and more diverse and complicated technology. Big Data, which allows for extraction of data from every imaginable source, offers a comprehensive approximation of data, which allows for early fraud detection and keeps possible damages to a minimum.

Credit Management

Big Data provides greater predictive ability, since new sources of data (social media, marketing databases, etc.) allow much better predictions of user behavior and can anticipate problems with repayment or detect fraud early.

Money Laundering

Incidents are identified quickly and a broader perspective of the situation is offered, which allows for reactions in almost real time.

Market and Commercial Loans

Big Data allows for better business and market simulations and predictions, such as interest rates, exchange rates, liquidity, and raw material prices.

Operational Risk

Big Data’s potential power lies in its ability to integrate a variety of platforms into a single solution, which provides more control and information about client interactions, while improving security and confidentiality.

Integrated Risk Management

In summary, Big Data offers the ability to provide a global vision of the different factors and areas related to financial risk.

In what risk management activities has Big Data had the greatest success?

According to a survey by The Economist Intelligence Unit, businesses and entities that use Big Data tools report that the risk management activity for which Big Data has been the most successful is the prevention of credit card fraud, indicated by 31% of survey participants. The other two activities that showed the greatest benefits were the evaluation of credit repayment risk, or prevention of defaults, at 26%, and the ability to compile and analyze liquidity requirements, at 24%. Other lesser-mentioned but also positive benefits of Big Data include assistance in regulatory compliance and reporting (9%) and prediction of market trends (7%).

Opportunities for Big Data

Big Data opens up an infinite number of opportunities for every industry, but there are three that most effectively demonstrate its potential in the current market: quick contact with clients to verify suspicious activity, which can be done in almost real time; the use of predictive models to detect fraudulent transactions, which are becoming more refined and effective every day; and payment behavior tracking for all transactions, offering greater traceability and more diverse data sources.

Conclusions

For the financial sector, markets are becoming more and more interconnected, thus increasing risk. However, given that the available data for analysis is also increasing exponentially day by day, Big Data provides more thorough and detailed information regarding this sector and its tendencies.

The most frequent applications of Big Data are the predictive models used to avoid fraud and the tracking and analysis of user behavior for credit risk management purposes. There are other areas in this industry where Big Data applications have great potential, but they are still in the initial phases of development and implementation.

There is a huge potential for Big Data applications in the FinTech industry as it relates to risk detection and opportunities for new technologies and payment systems. The growth of these new systems comes with an increase in risks, but they are now much more predictable from the provided information due to the availability of data on the web.