Data science and machine learning projects take too long for an organization to wait for results. In some cases, the specific direction of the planned work changes to make the entire project obsolete. Express delivery takes on unprecedented importance from this point of view, which has proven extremely impossible for many data science companies. CyborgIntell, since its inception, has been aware of this fact and has therefore changed the way data science/machine learning projects are undertaken in the course of time. Its iTuring is one of the zero-code and data science/machine learning platforms that its customers find to be effective. Analytics Insight shared an exclusive interview with Suman Singh, Founder and CEO, CyberGentle.
1. What is the mission and objectives for which the company was created? In short, tell us about your journey since the establishment of the company.
CyborgIntell is built with a design to help organizations seamlessly adopt AI using automated data science and machine learning platforms and accelerate business intelligence and decision-making by reaping the full potential of their data in a faster, transparent, and accurate way. Our journey has been very impressive in terms of supporting financial institutions to achieve the benefit of AI and improve their bottom line. It is worth noting that few of our clients got more than 200% ROI using our cutting edge AI platform.
2. Please list some of the main challenges the company has faced so far.
Data-driven decisions and intelligence solutions must be backed by solid evidence and numbers to show the actual impact. Hence getting a head start from each of our valued customers and proving ourselves in this business was quite a challenge. From building a comprehensive comprehensive product with every minute detail, to gaining trust from potential customers is an achievement in itself. Pursuing that elusive dream of building machine learning models with the click of a button in just a few hours and turning them into reality over the course of these 4 years, we have been fortunate to hear positive feedback from clients and potential clients like “Also it’s good to be honest”. We’ve been asked time and time again, “How is your system equipped to do this marvel of deploying data science projects in just a few weeks?”
3. What is the biggest advantage of USP that distinguishes your company from competitors?
CyborgIntell understands the complexity of data in the financial services space and how the right data-driven solutions can bring the critical differentiation factor that is much needed in this sector. It is a clever fusion of in-depth field presentation of the micro-tech sector, the precision with which our engineers designed and constantly upgraded our AI platform and expert data scientists at their helm who have been able to create a world-class revolutionary platform that is the first of its kind – “iTuring”. iTuring’s proprietary platform is a code-free, AI-driven, data science and machine learning-driven platform that enables organizations to flawlessly develop, deploy, operate and manage the risk of sophisticated machine learning models on a single platform.
4. Please let us know what products/services/solutions you offer to your customers and how they benefit from them.
As mentioned above, CyborgIntell’s flagship product “iTuring” is a fully automated machine learning platform for data science, which has come as a relief to financial institutions, increasing loan approval rates by 20-30% and drastically reducing customer acquisition cost by 40 to 50 % and convert debt collection success rate quite easily. CyborgIntell helps power AI and deploy AI models 80 times faster. This in turn helps in increasing the return on investment using AI and realizing the value of machine learning models. CyborgIntell has developed a specialized solution, especially for financial institutions to record more revenue and prevent revenue leakage in a smarter and faster way, which is highly cost-effective.
5. What are the main trends driving growth in Big Data Analytics/Artificial Intelligence/Machine Learning?
Some of the major trends we see in the industry are automated machine learning platforms that allow the business user to categorize, verify and target forecasts at the time of need, which can be a game changer for banking institutions in India and across the globe. New age banking and lending startups have already begun deploying a wide range of no-code, self-learning, AI/machine learning platforms that cover fraud detection, risk management, and customer acquisition without the need for a complex technology adoption curve. Scammers are notorious for applying various 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 deterioration in the previous model very quickly and save huge fraud losses.
6. What concerns do organizations have before using Analytics?
Companies are reluctant to make a big leap due to their insufficient understanding of big data. Data professionals can work their way around a massive amount of data and come up with a crystal clear story, but others may not get a transparent picture unless they trust and exploit the entire process. Most of the time their data is disorganized and they don’t know how to store, process, merge and pass usable data. In stark contrast, companies opting for new technologies ensure that they will not be left behind and grow exponentially. Data security is another pressing concern when it comes to sensitive financial data. Organizations are sometimes wary of embracing AI and may find it hard to believe that we can extract value from their data. Another concern can be the cost and maintenance fear of using Analytics to drive their business, as data in competent hands can result in a significant loss of time and money.
7. What industry sectors are you currently focusing on? What is your market entry strategy?
The current focus is on the BFSI segment, given the huge potential it offers in a growing market like India. The total fintech opportunity in India is set to rise to $1.3 trillion by 2025, according to Inc42’s State of Indian Fintech Report, Q2 2022. We see that the available data is not being used optimally which creates a lot of hurdles for the company to reach its full potential. With our offices in Bangalore, Johannesburg and Dallas, CyborgIntell has processed more than 170 terabytes of data, implemented 50 million plus real-time predictions using our powerful AI technology with over 127 use cases delivered, and over millions were created. of machine learning models. Our client 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 of the partners with whom CyborgIntell is closely associated, to drive growth together.
8. Would you like to highlight some of the use cases where structured analytics have benefited greatly
Optimizing Groups and Optimizing Efforts for FinTech Companies
a challenge – The NBFC sector has undergone a major digital transformation over the past few years and plays an important role in the growth of any financial system. They are now more customer centric than ever and are taking the time to understand customer behavior, build customized products, reach different customer segments with customized loans and customer friendly payment plans, and take on higher risks at a much faster pace, given the competition for market share in FinTech. However, this brings a new set of challenges such as debt collection. Debt collection is important for the company to improve its cash flow and prevent revenue leakage which in turn can help companies reduce the risk of incurring losses and free up their resources for the growth of the company.
The solution – iTuring from CyborgIntell 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 predict the movement of late payments for the entire portfolio, across all customers and all payment repositories. The outputs and interpretations of hypothetical prediction models about customer behavior can help identify strategies to improve overall collection efforts and, as a consequence, portfolio optimization.
The FinTech we dealt with in group optimization had a default rate of ~12%. We used iTuring to build their predictive models that can predict customer movements from one delinquency group to another for pre-late, early stage, late stage, and recovery. iTuring has developed accurate models that forecast default in the immediate following month with an accuracy of ~86%, enabling companies to effectively manage their monthly collection portfolio. This greatly benefited the company and helped it identify 9 customer segments based on PD and VaR and develop collection strategies around them. By simply focusing their efforts on the 72% of potential defaulters identified in the top 30% of customers, the company could improve collection by 116%.
Increase lead conversion
a challenge – Leads are the most important aspect of any marketing strategy and lead generation organizations cannot maneuver around sales and expand their business. Potential customers are the starting point for reaching potential customers. However, the main challenge for organizations today is interacting and reaching the right customers at the right time using different touch points to improve lead conversion and customer experience. Being able to identify the right target segment and appropriate offers for promotional 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, which come at a price, given the cost of customer acquisition these days.
solution – With iTuring, you can create highly accurate predictive models within a couple of hours. These models can predict the likelihood of a potential customer becoming a customer. This information can be used to reach potential customers who are wanted and who are most likely to buy your product, thus improving the results of marketing campaigns. In addition, iTuring models can also predict customer sensitivity to price, thus ensuring that you are offering customers the right price they want to buy at. Model results not only help you plan marketing campaigns effectively, but also help you effectively adapt your key buying strategy.
For the insurance aggregator, use CyborgIntell iTuring to build a “key conversion model” and identify the leads with the highest probability of converting into successful customers, which in turn nurtures the business, giving it the right momentum and keeping it going. Companies used their model results and increased their lead conversion rate by 1.92 times. As a next step, they will use the results of the model to increase their remote communication efforts by 50% to achieve a 300% increase in conversions by focusing on high quality leads and that means high value customers.
An exclusive interview with Suman Singh, founder and CEO of CyborgIntell, appeared for the first time.
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