How Big Data Analytics can make credit risk assessment more efficient and accurate

It is essential that fintech companies regulate their risk exposure and keep it within acceptable and reasonable benchmarks. At the same time, they should seek to gain the maximum possible rates of return after adjustments of risks. Being overly lenient can be even worse than being overly late with evaluations and acceptance of loan applications.

The key to making correct decisions lies in the quality and speed of credit risk assessment. The lack of sufficient data,  improper data analysis by credit risk assessors and a time-consuming evaluation process are some of the factors that often make credit risk assessment difficult and sometimes imprecise.

Fortunately, with the use of Big Data Analytics you can minimise these risks, speed up the process and make it more accurate. Let’s take a closer look at how financial institutions can benefit from it.

What is credit risk assessment?

Credit risk assessment is key to the success of fintech companies. They need to evaluate the creditworthiness of individuals as well as the corporations to whom they provide credit.

The main goal of credit risk assessment is to compare the risk versus reward ratio of lending to specific individuals and companies in order to make informed credit decision.

Credit risk assessment involves the following steps:

• Analysis of the credit history of the applicant
• Evaluating the capacity to pay back the borrowed capital
• Putting into perspective the amount of capital to be borrowed
• Taking into account governmental and organisational regulations
• The worth of collaterals, if any

What are the challenges faced by financial companies in credit risk assessment?

Credit risk assessment brings many difficulties that must be dealt with by the use of all the available tools. Here are some of the problems that are commonly faced by fintech companies providing loans:

Maintaining a high level of quality and efficiency

The fact is that the risks involved in lending can’t be eliminated in their entirety. No matter how good the credit scores of the borrower are, there will always be a possibility of non-payment of loans because of numerous external factors.

However, the quality and efficiency of the credit risk assessment will ensure that most decisions made regarding credit are correct. Big Data Analytics can be of great help in this regard.

The other challenge in credit risk assessment is optimising the time taken for evaluation. Excessive time in the examination can push the borrower to competitors. So, the right balance needs to be struck between high-quality credit risk assessment and the time in which it is completed.  Also in this regard, Big Data Analytics can be beneficial.

Compliance with regulatory and statutory provisions

After the global financial crisis in 2008, regulatory and statutory provisions related to credit risk management have become more stringent, posing another difficulty in credit risk assessment.

However, the fundamental differences between traditional financial institutions and fintech companies have created statutory ambiguity as far as the application of some legal provisions is concerned. It is apt to say that statutory amendments in most countries have not been able to keep pace with the rate at which fintech companies have grown.

In any case, the credit risk assessment must be compliant with government regulations, it must reduce the probability of non-repayment of loans, and it must be done in minimal time.

At this point, Big Data Analytics come into the picture. Evaluation of loan applications presented before fintech companies become quicker and minimises risks while adhering to relevant laws.

Absence of sufficient data

In a world that creates 2.5 quintillion bytes of data every day, the idea of insufficient data might seem absurd, but it is a fact. It is not caused by the unavailability of data but failure to access it.  Gathering information might equal the cost of credit risk assessment, but if it is used properly it can provide significant accuracy when issuing loans to creditworthy borrowers. The value of data gets reduced considerably if it is not present in the desired volumes.

Inefficient management and usage of data

Analysis of the available data, whether it is structured or unstructured, is essential to the process of credit risk assessment. FinTech companies need to collect all the relevant information about their customers. Creating relatively small groups to understand general customer behaviour plays an essential role in credit risk assessment.

Not using optimum tools for credit risk assessment

Leveraging the latest technology for credit risk assessment and the analysis of the available customer information, is essential. Not making use of these tools is always going to increase the chances of risky loans issuance with a greater probability of defaults.

Improper analysis by credit risk assessors

The manual assessment of loan applications and customer information can pose risks. The ability of human assessors is always going to be limited in terms of accuracy as well as speed. FinTech companies in the lending business can get thousands of applications in a week and employing sufficient human resources for evaluation can be a costly affair.

How can Big Data Analytics improve credit risk assessment? 

The role of Big Data Analytics in credit risk assessment is significantly vital. The use of this technology is the answer to almost all problems that fintech companies face in this area.

Big Data Analytics will ensure that fintech companies have all the information they need about specific customer groups as well as individuals. Once the problem of insufficient data is solved, proper analysis of the available information becomes essential to the process of credit risk assessment.

The ability of Big Data Analytics to assess and analyse high volumes of data in minimal time gives an edge to fintech companies utilising this technology.

It is also important to find out whether the financial condition of the borrower changes after the loan has been issued. The payment behaviour of the customer, their communication with other financial service providers and social media activities can be beneficial in assessing the changes.

Big Data Analytics not only provide all the relevant data, but they also analyse it accurately to provide considerably improved evaluations of credit risk.

Fintech companies can also broaden their customer base by using Big Data Analytics. In the past, financial institutions avoided providing loans to people with no credit history. Today, social media, as well as mobile data, can be used to assess the creditworthiness of people in this category. Fintech companies can use this information to minimise their risks. They can also consider marketing their services to potential borrowers with the lowest default risks by providing attractive and flexible offers of credit.

What is Spyrosoft’s approach to credit risk assessment? 

At Spyrosoft, we are committed to creating world-class credit risk assessment mechanisms for our clients requiring these services.  Fintech corporations making use of Big Data Analytics can always stay on top of credit risk assessment because they are never short on information about  potential borrowers.

We can help to lower default rates for future loans by processing and analysing all relevant datasets, leading to the highest standards of credit evaluation.  Our services will help you to keep track of all changes in the financial position of individuals and corporations to whom credit has been issued.

If the current status of their finances reflects a probability of default, you can devise strategies to deal with the situation. By using Big Data Analytics, you can be sure to expand your business by catering to the needs of low-risk borrowers, even if they do not have a quantifiable credit history and credit ratings.

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