We highlight – via a series of posts on Credit Risk Management (Read the prelude – “Risk Management : How Can we Help You?“) – the ever-growing challenge of credit risk in financial institutions, the benefits of real-time analytics and the wave of change that can be brought about with Heckyl’s unique capabilities. We bring to you the first post, from the series.
Exposure to the probability of default by their debtors, also called as Credit Risk, is the major risk the global banking system is facing today. In the current volatile market condition, where Indian companies are confronted with issues like demand slowdown, unfinished projects set up at inflated costs, lengthening working capital cycle squeezing cash flows, it is not uncommon for an institution to default on payments. This leads to huge non-performing loans sitting on the banks’ balance sheets.
Banks not only need to make careful considerations before lending out to new loan applicants, but are also expected to manage this risk and balance it with sustainable profitability. At the same time, the existing (Basel II, Dodd Frank, etc.) and evolving (Basel III) regulatory accords have put an additional pressure on banks to meet higher capital requirements and liquidity buffers.
This issue of non-performing assets is likely to get worse due to the global economic slowdown impacting most segments across bank’s portfolio like micro, small and medium enterprises, large corporates and agriculture to name a few. The extent of the challenge is such that non-action is no more an option for public and private banks. The need of the hour for financial institutions is to overcome the short-sighted vision of viewing credit risk management as purely a regulatory exercise and build on this opportunity to improve overall performance and secure a competitive advantage.
Credit Risk Management aims at mitigating the losses that might arise out of a borrower defaulting on the payment by understanding the adequacy of a bank’s capital and loan loss reserves at any given time. The lack of an efficient method to target credit risk management has long been a challenge for financial institutions.
The question here is: With the changing landscape of the amount of data available on the web, can we bridge the gap between untapped data and addressing this challenge of managing credit risk?
Globally, data is exploding at a rapid pace. For example: 500 million tweets per day, 1 billion unique active users on Facebook. The key drivers for this explosion of data are the switch from analog to digital technologies, increasing penetration of smartphones and the rapid increase in data generation by individuals and corporations alike. The boom of Internet of Things will mean that the amount of devices connected to the internet will rise to 50 billion by 2020. The rise of structured and especially unstructured data like photos, videos and social media has ushered us in an urgent need to tap this data and study its structure and patterns. Research shows that the amount of digital information in existence will have grown from the current 4.4 zettabytes to around 44 zettabytes (= 44 trillion gigabytes) by 2020. (Source : Forbes, 2015)
The deterring points for financial institutions today are the complexities of fetching these huge data sets, dealing with it and making insightful sense out of it. This is where Heckyl’s unique capabilities come into the picture. Heckyl sources real-time data from more than 1.5 million sources which are relevant to the financial industry. The sources include (but not limited to) government agencies, central banks, company websites, media houses, Twitter, Facebook, blogs etc. Using machine learning algorithms and natural language processing (NLP) techniques, Heckyl is able to tag each news item to various categories/red flags which are relevant for a credit analyst.
Heckyl strongly believes that it is time for financial institutions to realise that the solution to their challenge lies in the plethora of data sets available across the web. Building on humungous data, curating it based on the needs of the financial institutions, a comprehensive approach to NPA management can be evolved, that not only comprises of curative, but also preventive actions across the credit life-cycle. The preventive action aims at studying the pre-default behaviour of customers and define triggers equipping the credit risk manager/ analyst with real-time feeds on companies and sectors where the bank’s exposure lies. Early warning signals can then be raised by the risk manager or analyst ,based on the intelligent insights provided by the system.
Watch this space for other upcoming posts on Credit Risk Management…