There is no doubt that traditional banking, both in Latin America and in the rest of the world, has played an important role in credit risk management. However, implementation and performance models have lagged behind the application of new, more effective technologies. While fintech and online companies process loans without collateral, requesting few documents and in five minutes, banks are still adapting to this reality. For these financial institutions should move quickly to use with big data, Artificial Intelligence (AI), augmented reality, deep or automatic learning and APIs of machine learning, and so their risks are controllable. 

Challenges of risk management 

Within the traditional credit risk management schemes, banks have a wide variety of tools to analyze personal and business data. To guarantee these operations, they use a credit rating or evaluation corresponding to each person, entity, company or country. 

This assessment, which is carried out through different evaluation processes, warns the bank about the ability to pay or non-payment and is currently a process that takes a certain time depending on the loan, which has fostered a new culture of micro-credits approved in minutes and almost no requirements. 

That is why innovative forms of real-time credit risk management represent an important initiative for users. Although it is a small aspect within the wide range of financial products, it highlights the lack of transition of the majority of Latino banks. The younger connected generations are becoming more demanding and expect greater versatility in terms of these products and services. A more immediate availability of approval for different credit options highlights the importance of ICTs in risk management. 

Risk management is not only restricted to the domain of banking only, but it is requirement for other industries as well such as money service business. Better money service business risk assessments are vital for MSBs as well as their clients.   

Big data solutions and other technologies 

Perhaps faster than anticipated, digitization is transforming business models dramatically. Due to a substantial increase in the generation, interpretation, protection and filtration of data, more efficient, powerful platforms or APIs with greater analysis capabilities are required. 

The data big represents a tool for variable broad spectrum solve certain associated with managing credit risk. Through its various applications, Latin American and global banking has more accurate forecasting models that include a real-time evaluation capacity. To this speed of response are added applications of voice recognition, augmented reality, AI, fog and edge computing, among others. Everything is intended to meet the demands of information processing, facilitate processes, generate more immediacy and have control mechanisms with better overall performance. 

Fintech companies are well aware of the scope of this automation, as it provides a broader view of the possible risks involved. In this way, they can guarantee credits, loans and other services almost instantly because they have predictive tools to know in more depth the variables of default or not. 

The bank has the resources to take advantage of the speed, volume, diversity, value and accuracy of the data with this technology. This not only allows you to save costs in terms of digitization, but also to offer better financial products with availability that does not require such a long wait. The software integrated with the qualities of big data has a more developed capacity to predict credit behaviors. 

The records are managed in a few minutes, which allows banks and financial companies to anticipate with greater objectivity the real possibilities of return, fraud or money laundering. 

Risk management trends 

If traditional banking wants to be part of Industry 4.0, organizations have to be in tune with emerging technologies. In many cases, the rise of automation will give way to cognitive initiatives based on artificial intelligence that will replace risk decisions to some degree. On the other hand, behavioral science continues to develop models to define the perception of risks. 

These advances are promoting a better understanding of the various assessments related to this type of management, improving decision-making regarding various credit aspects. Assuming that the economy moves towards a network structure, the collective management of data will have greater preponderance. 

Under this context, risks become more tangible, measurable and quantifiable as performance values. This gives banks a better ability to determine the appropriate levels of certain policies. Finally, the disruption of emerging technologies dictates the transformation of traditional business models. 

Hyper-connectivity amplifies the voice of the user and spreads their perception of the values, services and reputation of financial companies in various digital channels. If the field of credit risk management and companies’ dependent on this area want to survive, they must implement strategic measures that take into account these inevitable factors of change. 

The tools that the businesses need to implement shouldn’t be looked at as an expense – they are an investment because they will provide a lot of benefits to both the organizations and their customers. Businesses will be able to curb fraud and scams easily while customers will get much faster and more convenient financial services. Risk management is too important to be left dependent on the vigilance of a few people – it needs all the technology and power that it can get. 

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