Exclusive Interview with Suman Singh, Founder, and CEO, CyborgIntell

Data science and machine learning projects take too much time for an enterprise to wait for the results to show up. In certain cases, the very direction the business is planned changes to make the entire project obsolete. Quick delivery takes unprecedented significance from this point of view, which has proven to be highly impossible for many data science companies. CyborgIntell, since its inception, was aware of this fact and so has transformed the way data science/ML projects are executed within time. Its iTuring is one of a kind zero-code, Data Science/Machine Learning platform that its clients find effective. Analytics Insight has engaged in an exclusive interview with Suman Singh, Founder, and CEO, CyborgIntell.

1. With what mission and objectives, the company was set up? In short, tell us about your journey since the inception of the company.

CyborgIntell has been founded with the resolve to help enterprises seamlessly adopt AI using automated data science and machine learning platforms and accelerate their process of Business Intelligence and decision making by reaping maximum potential from their data in a faster, transparent, and accurate manner.  Our journey has been quite impressive in terms of supporting financial institutions to realize the benefit of AI and improve their bottom lines. It is noteworthy that few of our customers have got more than 200% return on investment using our cutting-edge AI platform.

2. Kindly mention some of the major challenges the company has faced till now.

Data-driven decisions and intelligence solutions have to be backed up by strong evidence and numbers to show the actual impact. Hence to get a head-start from each one of our valuable clients and prove ourselves in this business was quite a challenge. From building the all-encompassing exhaustive product with every minute detail, to gaining trust from prospective customers is quite a feat by itself. By chasing this far-fetched dream of building machine-learning models with a click of a button within a few hours and transforming it into a reality over these four years, we have been lucky to hear positive feedbacks from clients and prospective customers like "too good to be true". We have been asked time and again, "How is your system equipped to do this wonder of deploying Data Science projects in just a few weeks?"

3. What is your biggest USP that differentiates the company from competitors?

CyborgIntell understands the complexity of data in the financial services space and how the right data-driven solutions can bring in the much-needed critical differentiation factor to this sector. It is an intelligent amalgamation of an in-depth Domain view of the fin-tech sector, the precision with which our architects have engineered our AI platform and constantly upgrading it and the expert data scientists at the helm that we have been able to create a truly world-class revolutionary first of its kind flagship platform – "iTuring". The proprietary "iTuring" platform is a no-code, AI-driven, data science and machine learning platform that enables enterprises to develop, deploy, operationalize and manage the risk of sophisticated machine learning models impeccably on a single platform.

4. Please brief us about the products/services/solutions you provide to your customers and how they get value out of it.

As mentioned above, CyborgIntell's flagship product "iTuring" is a fully automated Data Science Machine Learning platform, which has come as a relief to financial institutions, augmenting loan approval rates by 20 to 30% and drastically cutting down the customer acquisition cost by 40 to 50% and turning around the success ratio of debt collection quite effortlessly. CyborgIntell helps in operationalizing AI and deploying AI models 80 times faster. This in turn helps in driving ROI with AI and realizing the value of machine learning models. CyborgIntell has developed a niche solution, especially for financial institutions to clock in more revenue and prevent revenue leakages in a much smarter and faster way, which is highly cost-effective.

5. What are the key trends driving the growth in Big Data analytics/AI/Machine Learning?

Some of the key trends that we see in the industry are Automated machine learning platforms that allow the business user to categorize, validate and target prospects at the time of need, which can be a game-changer for banking institutions in India and worldwide. New-age banking and lending start-ups have already started deploying a wide-scale of self-learning and no-code AI/ML technology platforms covering fraud detection, risk management, and customer acquisition without the need for a complicated technology adoption curve. Fraudsters have been quite notorious for applying different fraudulent means but due to Automated AI and self-learning AI, payment industries can deploy advanced ML to capture early fraudulent trends and automatically improve the model if there is any degradation in the previous model very quickly and save massive fraud losses.

6. What are the concerns that organizations have before using Analytics?

Companies hesitate to make the big jump given their insufficient understanding of massive data. Data professionals can work their way around a humungous amount of data and make a crystal-clear story out of it, but others might not get a transparent picture unless they trust and harness the whole process. Most of the time data is unstructured and they do not know how to store, process, integrate, and pass on usable data. In stark contrast, companies that choose modern techniques ensure that they do not get left behind and grow exponentially. Data security is another pressing concern when it comes to sensitive financial data. Organizations are sometimes wary of adopting AI and may find it hard to believe that we can extract value from their data. Another concern can be the fear of cost and maintenance for using Analytics to drive their businesses, as data in competent hands can result in a huge loss of time and money.

7. Which industry verticals are you currently focusing on? And what is your go-to-market strategy for the same?

The current focus has been on the BFSI sector, due to the huge potential it offers in a growing market like India. India's total fintech opportunity is set to rise to $1.3 Tn by 2025, according to Inc42's State of Indian Fintech Report, Q2 2022. We see that the data available is not used optimally which creates a lot of hurdles for the firm to reach its full potential. With our offices based in Bangalore, Johannesburg, and Dallas, CyborgIntell has processed more than 170TB of data, done 50 million plus real-time predictions using our robust AI technology with more than 127 use-cases being delivered, and also built more than millions of machine learning models. Our customer base includes Tier-1 banks, Tier-1, and Tier-2 insurance, digital lending, and housing finance companies. To name a few, HPE, True North Partners, and Sequentis are a few partners that CyborgIntell is closely associated with, to drive growth together.

8. Would you like to highlight a few use cases where analytics has benefitted the organization tremendously

Improving Collections and optimizing efforts for FinTechs

Challenge – The NBFC sector has undergone a significant digital transformation over the last few years and plays a crucial role in the growth of any financial system. Now they are more customer-centric than ever before and take the time to understand customer behavior and build customized products and reach out to different segments of customers with customized loans and customer-friendly repayment plans and take higher risks, at a much faster pace, given the competition for market share in the fintech. This however brings in a new set of challenges like debt collection. Debt collection is important for the company to improve their cash flow and prevent revenue leakages which in turn can help businesses reduce the risks of incurring losses and free up their resources for the growth of the company.

The Solution – CyborgIntell's iTuring can be used to develop predictive models that can make the right decision with minimal time and effort, and help identify customer defaults early in their lending journey. This can accurately forecast delinquency movement for the whole portfolio, across all customers and all payment buckets. The outputs of the default prediction models and their explanations around customer behavior can help define strategies to improve overall collection efforts and as a result, improve the portfolio.

The FinTech company we engaged with on Collection optimization was experiencing a default rate of ~12%. We used iTuring to build predictive models for them that could predict customer movements from one delinquency bucket to the next for pre-delinquency, early stage, late stage, and recovery. iTuring developed accurate models which predicted default in the immediate next month with an accuracy of ~86%, enabling businesses to effectively manage their monthly collection portfolio. This has benefited the company in a big way and helped them identify 9 customer segments based on probability of default and value at risk and develop collection strategies around the same. By simply concentrating their efforts on 72% of likely defaulters that were identified in the top 30% of customers the company could improve collections by 116%.

Increase Lead Conversion

Challenge – Leads are the most important aspect of any marketing strategy and without lead generation organizations cannot maneuver around sales and expand their businesses. Leads represent the starting point for reaching out to potential customers. However, a major challenge for organizations today is reacting and reaching out to the right customers at the right time using various touch points to improve lead conversion and customer experience. The ability to identify the right target segment and right offerings for promotion campaigns is a common business objective in every industry, be it banking, insurance, or retail. This prevents aimless wandering trying to find the right customers, who come at a price, given the cost of customer acquisition these days.

Solution – With iTuring, you can build extremely accurate predictive models in a couple of hours. These models can predict the likelihood of a lead turning into a customer. This information can be used to reach out to those desired leads who are more likely to buy your product, thereby improving the results of marketing campaigns. Additionally, iTuring's models can also predict the customers' sensitivity to price, hence ensuring that you offer customers the right price at which they are willing to buy. The results from the models not only help you plan marketing campaigns effectively but also adapt your lead procurement strategy effectively.

For an insurance aggregator, CyborgIntell used iTuring to build a "Lead Conversion Model" and identified leads with the highest likelihood to convert into successful customers, which in turn nurtures the business, giving it the right momentum and to keep it going. Businesses used the results of their model and increased their lead conversion rate by 1.92x. As a next step, they would be using the results of the model to increase their tele-calling efforts by 50% to achieve a 300% increase in conversions by focusing on high-quality prospects and which means high-value customers.