The contemporary world is powered by data and advanced technology. The concept of the global village has been further strengthened in the past decade with the proliferation of internet technology. Every industry that relies on the use of modern technology is leveraging data to grow exponentially. Be it the e-commerce industry, the marketing industry or the finance industry, they have all changed drastically in the last decade. Today we have tools like contextual banking and Robo-advisors only because we have a huge database to rely upon. Let’s delve deeper into the role of big data in banking.
Big Data In Banking
Banks and financial institutions today rely heavily on data to make more informed, improved and result-oriented decisions in the business. From analysing customer behaviour to predicting market trends and improving organisational processes, there is a whole lot that the banking industry is using data for. With the advent of AI and Machine Learning, improving internal process and increasing general efficiency has been a piece of cake. Robotic process automation technology also helps to automate a whole lot of work that was earlier performed by using human labour. This helps the organisation to save a lot of time, money and reduce the error to zero which is highly beneficial to players in the banking and finance industry. In addition to this, it also helps in boosting cyber security and eliminating the risk elements.
Data Challenge In Frontier Markets
After the global financial crisis, the banking and finance industry has been very rigid in terms of regulatory requirements to avoid any kind of unfair practice that may lead to future crises. Minimizing the credit risk has been a priority for all financial institutions. From a broader perspective, the process of credit risk assessment includes gathering relevant information about the borrower, analysing the information collected and then making a decision as to whether the credit risk profile of the borrower is acceptable or not. On the technical side of it, this requires applying various financial analysis techniques and predicting future cash flows based on the data obtained.
The whole credit risk analysis process can be only as good as the information collected about the borrowing party. In the case of frontier markets, collecting the relevant data or information is a challenge. The accuracy of information gathered is questionable, which further adds up to this blunder is the challenge of consistent analysis across the credit management system. A truly effective credit risk analysis requires the right kind of information
The Basel committee guidelines have set certain standards and regulations that are to be followed by banks to maintain a healthy global economy. These measures include maintaining the required capital reserve amount, putting a risk evaluating methodology in place and explaining the same to authorities. As a result of these benchmark standards, players in this industry require sufficient data to back their judgment, satisfy the regulatory bodies and maintain their presence in the global markets.
Apart from the seasoned players, there are many newbies in the industry especially in the frontier markets where there is comparatively less competition. The big players already have years of experience and relevant data to adhere to guidelines and make accurate predictions. When it comes to the nascent players in the industry they face the challenge of default data shortage for various asset classes and other relevant data from clients.
Another challenge that the banks or other financial institutions face in the frontier markets is having a proper model in place to analyse both quantitative and qualitative aspects of the data gathered. Quantitative data can be easily evaluated and assessed, when it comes to qualitative measures it’s a tough nut to crack. For example, how do we compare and incorporate the effect of weak management vs. robust management practice? The need to build models that can easily incorporate qualitative aspects of information is paramount.