Introduction
Automation is a priority in the minds of bankers as they deal with growing regulatory complexities and increased competition. They look to achieve best customer experience, optimal cost efficiency and absolute compliance through the use of automation. The advent of deep technologies such as AI, ML & NLP have brought about that wave. Here, we look to gain a deeper understanding about the impact these technologies have in the financial services industry and how Crediwatch is employing AI, ML and NLP into its products.
AI has come of age and has progressed by leaps and bounds across all industries such as financial services, retail, healthcare and manufacturing. Another huge advancement in recent times has been the ability of AI to learn without any constant supervision which is known as Machine Learning. Machine Learning brings immense value addition to our existing knowledge set. It gives its users a unique competitive edge and boosts the productivity of these.
Machine learning has great uses in the financial services industry today. It is used for real time credit risk modelling where diverse data sets are integrated to arrive at a prediction for potential credit risk.
Machine learning has great uses in the financial services industry today. It is used for real time credit risk modelling where diverse data sets are integrated to arrive at a prediction for potential credit risk. It is used for detecting fraud and consequent fraud recovery processing. Investment predictions have largely moved away from the traditional methods and have begun using machine learning algorithms to predict the market.
NLP is used in the financial services industry today to streamline KYC processes wherein these systems are used to analyse changes in regulations and laws and intimate the people working within that particular domain/process about the change.
NLP enables extraction of information coded in natural languages. NLP is used in the financial services industry today to streamline KYC processes wherein these systems are used to analyse changes in regulations and laws and intimate the people working within that particular domain/process about the change. This accelerates regulatory response and streamlines the compliance process.
How are these technologies used in the Crediwatch platform today?
Crediwatch is a platform that services the entire lifecycle of the credit decisioning process in order to assist banks and NBFCs.
During the on-boarding phase, the KYC Module within our platform helps collect alternative data from public and regulatory sources and enables the identification and verification of entities and individuals. The platform also includes various AML solutions such as identifying and screening UBOs against various Sanctions and PEP lists across the globe.
The platform also includes various AML solutions such as identifying and screening UBOs against various Sanctions and PEP lists across the globe.
During the underwriting phase, the platform helps banks make effective credit risk decisions by providing them with the financial data and employing proprietary risk models on non-financial data related to the entity. Crediwatch has developed an Adverse Media Engine which collects over 40 million media articles from reliable domestic, international and industry-specific media.
Our proprietary algorithms conduct advanced sentiment analysis in order to help screen for any adverse news on entities.
Our proprietary algorithms conduct advanced sentiment analysis in order to help screen for any adverse news on entities. Through AI, we are able to sift through millions of articles to surface those that are relevant to the entity of interest and also score the sentiment of the article too. Crediwatch also aggregates Litigation profiles for corporate entities sourcing data from over all courts and their respective benches.
Our post-decisioning module includes an Early Warning System for credit risk monitoring and action. Machine learning is used for detection of leading signals indicating potential delinquencies and even default by a customer.
Business Impact
MSMEs across developing countries face a credit crunch of $2.1 – $2.6 trillion (₹135 – ₹166 trillion) equivalent to around 30-36% of the outstanding MSME credit in the world. Of which the Indian MSME sector requires a staggering $0.75 trillion (₹48 trillion)because of their lack of access to formal financing facilities. Crediwatch has understood the needs of this segment and has built a platform that helps leading financial institutions tap into this huge under-served market.
Crediwatch enables easy integration with loan origination systems within financial institutions. The Crediwatch platform is capable of handling requests at scale and hence allows financial institutions to make credit decisions in minutes. This results in scalability along with high cost optimizations and increased bottom lines.
The platform reduces KYC times because of the automated processes and reduces errors. This boosts the customer experience and reduces the costs involved. Crediwatch’s focus on AI enables it to tap 2500 public sources of data points and create a singular source of truth due to which Crediwatch seeks to become an easy gateway for accurate verification.
Conclusion
Artificial Intelligence has made great strides over the years bringing in tremendous applications for the financial services industry. From its use in KYC processes to applying ML in building credit risk models, these technologies have brought in various benefits. Crediwatch is committed to being at the forefront of these technologies to amplify the accuracy of decision making for financial institutions.
