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Big Data

Big Data for financial markets: regulatory bodies

The banking sector is one of the most regulated in the financial sphere, but the 2008 crisis left many doubts about the ability of regulatory bodies to prevent such disasters.

After the fall in value of many large banks (in 2008: Santander, BBVA, Popular, CaixaBank and Bankinter gave up 50% of their value), including the bankruptcy of some others (e.g. Lehman Brothers); regulatory agencies on both sides of the Atlantic had to negotiate to establish standards in both regulatory and accounting criteria.

It is thanks to these new criteria that the system began to see the light at the end of the tunnel, as the concept of "..." was included.expected lossThe "Provisions for Loan Loss Provisions", which obliges financial institutions to store certain provisions for each loan they grant.

This provision is included in order to cover possible losses arising from a defaulted loan. We say possible losses because we are keeping a percentage of each loan that is granted, without knowing with certainty whether the loan will be defaulted on. This very logical concept was practically not taken into account in the past, since financial institutions were not concerned about potential losses, but only about losses that had already occurred.

For the larger banks, some measures were included that prevent external supervisors from making the calculations on the provisions to be stored, these calculations are known as internal models, and they make a big difference for the institutions. For example: If the supervisors, generally applying the regulation, oblige us to make a provision of 20 % for mortgages with a high degree of delinquency, the internal models allow us to fine-tune these provisions, so that the different institutions can determine their provision according to their historical sales data.

As can be imagined, the big banks have immense historical stored data. To analyse and find value in these data, the following are used Big Data technologiesThe banks selected can, by analysing these sales histories, establish their own provisioning models based on their past experience.

For example: After applying analytics in a major bank, it is determined that mortgage loans, between 100,000-150,000 €, granted to men under 30 years of age, who have worked at least 5 years and with at least 20,000 € saved, have to have a provision of 10 % of the total credit. In contrast, the general models applied by supervisors stipulate that such loans must have a provision of 15 %.

If the selected bank submits these results to the supervisory bodies, it will be able to apply its internal provisions; so that, by using Big Data techniques and technologies, the bank in question has the possibility to provision less money on each loan, which will allow it to have more liquid money for its investments.

This need to processing of large amounts of data makes the financial sector a clear example of the use of Big Data technologies: until now, the leading banks had incorporated these technologies mainly for fraud detection and security issues; now all banks (large and small) are incorporating these technologies into their servers to have the means to answer to Europe for their actions (it is estimated that in the last 7 years, 10 large banks worldwide paid some 43 billion dollars in fines for not complying or not correctly reporting compliance with the CRR). This trend seems to be changing, as already in 2016, European banks invested an estimated $16 billion in software solutions alone.

Author: Iván Gómez Arnedo. Linkedin.

 

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