Power Of Data In Combating Credit Risk
While a couple of decades back, at its infancy, data research was severely restricted by lack of any form of data. Today in the information age, research is inundated by the volume, variety and velocity of data that a business generates.
Gone are the days when data was a simple series of numbers. Today effective data analysis combines number, text and images. To add to the potency of data research, new technologies like Internet Of Things (IoT) and Social Media platforms are driving mind-boggling volumes of consumer data.
So how then, can so much data help craft a focused business strategy?
Data is the most important element that powers Artificial Intelligence (AI) and large data sets make it possible for Machine Learning (ML) applications to learn independently, efficiently and rapidly. With use of data, Artificial Intelligence is helping organizations discover new insights from data that was formerly locked away in documents, blogs and images.
At times exponential growth in data also causes “Noise” in the data i.e. irrelevant information or randomness becoming part of the data. If the algorithm is too complex or flexible, then it can end up “memorizing the noise” instead of finding the signal and will result into over fitting of the model. Therefore, choice of algorithm is of utmost important to derive required inferences from the data accurately.
(Graph indicating data generated over the last two decades)
By 2025, IDC (International Data Corporation) predicts, worldwide data will grow by 61% to reach approximately 175 zettabyte. With as much data residing in the cloud as in data centers.
This volume of data poses complex challenges – The key challenge being that of exponential computing i.e. storage, processing, analyzing and interpreting voluminous data.
In the points mentioned below, we summarize how the problem of data overload has been addressed by organizations:
1. Exponential Database Storage
With emergence of Big Data, the challenge of storing massive data has been resolved to an extent; databases are becoming increasingly versatile and powerful. With availability of data in both structured and unstructured form, fast storing and retrieving of data have emerged as the most critical areas of Big Data architecture. Ultimately helping organizations to improve efficiency and make better decisions. In addition, a new type of database has emerged - Graph database, which is capable of discovering the hierarchical structure and visualize hidden relationships.
2 . Exponential Computational Power
The challenge of exponential computation was addressed through emergence of super computers; Supercomputers not only store huge volumes of data but can also process complex calculations very quickly. Faster processors can handle more calculations per second than slower ones and they're also better at handling really tough calculations. With computational power doubling about every 18–24 months, we can easily envisage the growth of the computing power and how it can become super intelligent with the help of Artificial Intelligence going well beyond our expectations.
Role of AI/ML in Credit Risk Analytics
In the world of Artificial Intelligence and Machine Learning we expect computers or machines to learn from past data, which in other words, implies learning from past experience; and then being able to perform the task intelligently like a human. In the recent years, many sectors have started using Artificial Intelligence tools to reduce human efforts and also to get efficient and faster results. Leading the charge of technology, banks are rapidly deploying cutting edge AI & ML to develop human like intelligence to mitigate credit risk and maximize their profits.
Risk Managers who had been monitoring Credit Risk via traditional and outdated ways are now applying AI/ML programs to their customer portfolios. Combining data interpreted by technology and insights generated by time spent dealing with customer’s offline, Risk Managers now have the unique ability to predict threats, customer delinquency and classify customers in High, Medium and Low Risk categories even before any loan is disbursed.
Banks also now have access to technologies like OCR reader, web crawling, social media integration that allows them to collect unstructured/semi-structured data through borrower’s financial statements, auditor reports, social media, customer review, news, etc. With the volume of data growing exponentially and rise of new statistical algorithms, banks can also accurately predict borrower behaviour in contrast to the traditional way.
With emergence of powerful algorithms like Neural Network, Hidden Markov Model and Ensemble Learning, it is imperative for banks to have huge volume of data to train the AI/ML model to compute the risk probability for each borrower accurately and accordingly alert the Risk Managers in advance.
About CARE Risk Solutions
At Care Risk Solutions, we specialise in Risk Management solutions for the banking vertical. We have an in house team of highly trained and experienced data scientists, working on powerful AI & ML products that support banks.
Our aim is to help banks automate their processes by adding a layer of human like intelligence between users and processes. Our AI/ML algorithms analyse and discover hidden patterns in data, which subsequently transforms data into actionable insights and helps banks improve their credit risk monitoring process.
Our products also help Risk Managers evaluate Risk scores of the borrower’s portfolio calculated on basis of various financial & non-financial parameters captured through various sources. Our Risk Management solutions assist the Risk Managers with valuable insights and detailed analysis, which enables them to take precautionary action from borrowers in the high-risk category.
All our models go through a regressive and iterative process to validate the outcome and accuracy of the model. The strategic implementation of Artificial Intelligence in banks helps them focus on every borrower’s credit portfolio and provides them with quick resolution for risk mitigation, while keeping their own risks at bay.