Crediwatch is a Bangalore based ‘Data Insights-as-a-service’ company that provides lenders and businesses with actionable credit intelligence on private entities they need to improve trust and increase their lending and trading activity.
Having provided insights on 50,000+ businesses and $7 billion loan portfolio, Crediwatch aims to fill this void by offering a dynamic “Trust Score” derived from millions of data points that are extracted and analysed across thousands of formal and alternative sources to help lenders assess borrowers and monitor them close to real time.
Sandeep Anandampillai is the Founder & CPO at Crediwatch. In an interaction with The Tech Pod, Sandeep speaks about the use of Artificial Intelligence in Law firms. Read More!
Tell us something about yourself and what does Crediwatch do?
A technology geek, bike enthusiast and a dog lover, I have over 14 years of global experience in software development, management and business on big-data projects for fortune companies with key focus on Product Management, Data Warehousing, Machine Learning at enterprises such as Oracle, ThoughtWorks and Schneider Electric.
Presently, I am the founder and Chief Product Officer of Crediwatch, which is a data insights-as-a-service platform that deploys scalable deep learning tools across disparate digital footprints left by big and small private entities. We provide actionable credit intelligence and dynamic credit management as a service to Financial Institutions. All of this is done with no human intervention by deploying AI/ML and NLP tools that provide the most reliable and comprehensive real-time insights on over 18 million risk profiles of companies and unregistered small businesses.
How are analytical tools helpful in managing huge amounts of data?
The analytical tools, specifically big data analytics tools, can help organisations improve their efficiency when it comes to operations, grow at scale and subsequently increase their revenues by an exponential multiplying factor. Using prescriptive analytics tools, companies can mine historical data for insights and use them to access profitable opportunities by gowing customer value and improving the overall product experience.
For example, Crediwatch uses sentiment analysis to spot and flag adverse news media sentiment for a business entity by analysing a large number of news articles. This wouldn’t have been possible without the use of machine learning tools.
How does API technology allow for easy integration?
To speak in the most layman terms, API is an intermediary that enables the conversation between two software applications in the language of data. Hence, API and integration are almost inseparable terms. API technology is the bedrock of Web 2.0. It has enabled easier software development by providing the fundamental building blocks. Now using APIs complex tasks can be accomplished with only a small and necessary amount of code. APIs, in conjunction with cloud technology, help in software integration at scale across entities. In one use case, this technology helps organizations with multiple technology systems use all of them in sync.
Crediwatch’s technology solutions are pre-integrated with APIs across multiple 3rd party sources, giving it the ability to automate several processes and offer scalable solutions. For example, the Onboarding solution uses APIs to digitally verify customer identifiers such as PAN/GST etc, perform several VideoKYC checks as well as integrate with the bank’s internal systems.
Can you simply explain the term ‘Supervised Machine Learning’?
In the simplest phrase, supervised machine learning can be called as learning by example. You train an algorithm on an already available dataset where both the inputs and outputs are present. And, then the algorithm evaluates new inputs to give outputs based on the learning from the “training” data. Obviously, this form of learning has its limitations as finding the best form of training data is a challenge.
How can one create an environment in Law Firms to use Artificial Intelligence?
While performing corporate due diligence, Law firms spend more time collating information rather than analysing it. This highlights the need for real time and reliable sources of information for the M&A due diligence processes conducted by firms for their clients. With tools like Machine Learning, several models can be created to evaluate financial and non-financial data related to businesses (e.g. peer evaluation, director changes etc). As law firms utilize such data more and more, there will be an increased adherence to removing human bias from due-diligence and corporate analysis.