Technology Applications for Corporate Risk Management

The technology allows companies to incorporate more robust procedures and have a predictive vision for corporate risk management, preventing fraud and other types of threats that can compromise their image and / or finances. 

The use of Big Data Analytics has revolutionized corporate risk management, since it helps to reduce costs with teams and other inputs, while providing precision and agility to the analyzes. This is because, with only one system, it is possible to unify the research sources and have always up- to- date data from the regulatory bodies of each sector (and all its rules, circulars and ordinances) and from other public entities, for example, conduct forecasts and have a more effective solution to prevent suspicious or fraudulent activities, grant credit or recover assets. 

In this article, we will delve deeper into the main applications of Big Data Analytics in corporate risk management. Read on and find out what they are and how to use them to protect your business. 

Big Data Analytics optimizes corporate risk management 

Among the numerous possibilities and benefits of Big Data Analytics tools in risk management, we can highlight: 

• Fraud identification.
• Prevention of money laundering and avoidance of foreign exchange.
• Letter of credit management.
• Credit grant simulations.

• Asset recovery (extrajudicial or already judicialized).
• Portfolio pricing.
• Measurement of degrees of default.
• Operational risk assessment.

How does the technology work? 

For the granting of credit, it is possible to analyze whether there are threats of non-payment of debts, that is, whether the assessed profile represents that of a ‘good’ payer or whether it may in some way be in default, or if there are values ​​that may remedy the acquired expense. 

It is possible to know the list of property and tangible assets (vehicles, real estate, aircraft) and intangible assets (brands, patents, software), for example. That’s because the more quality information you have about a potential customer, the greater the chance of a successful loan being validated. 

Credit recovery companies can also use the tool to define the debtor’s profile, the means he has to pay the debt – also to have lists with tangible and intangible assets and assets, including the list of lawsuits linked to each one – and until you find your current contacts and addresses. 

Other facilities are the pricing and segmentation of the portfolio, to facilitate the definition of objectives at the time of collection. Thus, one can understand which profiles should be contacted first, as they have resources and more ways to pay the amounts. 

In the case of recovery already filed, the collection of information about the debtor can also be procedural evidence since it is data from public and reliable sources. In the case of labor issues (or similar), it helps to find assets and / or corporate relationships for payments of the amount established in court. 

Regarding the assessment of organizational risks , more linked to corporate compliance and the prevention of money laundering, with the tools of Big Data Analytics, it is possible to analyze and validate data from new customers, partners and partners, suppliers and employees, in the most different operations. This makes it easier to identify in advance patterns of misconduct and the recurrence of actions to ensure that the occurrence is even fraudulent. 

One can, for example, track a company and / or an individual and understand how they are positioned in the market from different perspectives – cadastral, corporate, fiscal and tax, judicial and extrajudicial, labor, political (PEP), socio-environmental, among others – in addition to their possible employment, corporate and family relationships. 

And it is worth highlighting a very positive point of the technology: its ability to save resources, as it manages to optimize risk management. That is, even with a leaner team of analysts (be they Auditors, Risk Managers, Compliance Officers, Chief Information Officers or Chief Security Officers), it is possible to considerably reduce the potential of threats and in a much shorter time. 

Therefore, companies that invest in an optimized and safe process, with deeper analysis of a multitude of sources and available information, such as those performed through Big Data Analytics platforms, will more easily avoid financial and image losses, being a step ahead of the competition.