data-science-machine-learning

[data-science-machine-learning]

CyborgIntell seeks to simplify AI machine learning in order to improve business outcomes

India has the third largest fintech ecosystem in the world with The sector in India recorded a market size of $31 billion in 2021. The market has been steadily expanding due to the increase in digitization in the country. The recent entry of AI into the sector has further contributed to this growth.

Data as complex as it is of utmost importance especially to the BFSI market. The collection and correct usage of data can create a significant difference to the companies in the sector.

In order to help companies collect and process large amounts of data, help with the decision-making process and to assist companies to adopt AI technology; trio Suman Singh, Mohammed Nawas, and Amit Kumar founded CyborgIntell in 2019.

CyborgIntell is an enterprise AI software company for financial services, poised for simplifying AI & designed for better ROI. The company was set up to help companies take up AI using the company’s end-to-end Automated Data Science Machine Learning platform to accelerate the decision making process simpler and faster.

Co-founder and CEO Singh told Entrackr, “Our whole mission has been how we can simplify this AI and help our customer to generate a better ROI in a much faster, better and accurate manner.”

Among its key products are iTuring AutoML+ a zero-code auto AI that helps automate data science and machine learning, iTuring MLOps works along with the company’s DevOps platform and helps operationalize AI models for business impact, and iTuring Decision AI which helps organize the outputs of the predictive models and supplement them with policy driven rules.

Basically, iTuring cuts down turnaround time, aids in increasing efficiency, and assists in the delivery of new solutions, looking to increase a business’ ROI and save money by increasing precise and reliable business decisions.

The company has its headquarters in Bangalore, but it also operates offices in Johannesburg and Dallas. The platform licensing subscriptions generate the majority of the company’s revenue. It currently has around 40 active users.

A majority of CyborgIntell’s customer base consists of Tier-1 and Tier-2 banks, Tier-1 and Tier-2 insurance, digital lending, and housing finance companies.

The fintech company has received a total of $2 million in funding in its pre-series A funding round which saw participation from SenseAI, Pentathlon Ventures, and Ghosal Ventures.

The company is currently in discussions with fintech, insurance technology, and core banking solutions companies about developing analytical applications to extract value from their customer data through automation.

As part of its plans of expansion, the four-year-old company recently extended its operations to the US signing it a insurance company in North America. It further plans on expanding into new geographies within the North American market as well.

It currently faces competition from companies like Dataiku, DataRobot, and SAS. In order to get a stronger hold of the market globally as well, the company is also looking to tap into the Singapore, UAE, and Malaysia fintech markets.

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How AI is empowering the insurance sector to transform itself

The intelligent and well-informed utilisation of data, Artificial Intelligence (AI), and deep learning techniques are here to offer enormous growth opportunities to the insurance sector. These technologies are coming together to create ‘insurance as a product that is not only easy to use, and accurate but is also personalised, refined, and reduces overhead costs.

AI and automated data science machine learning platforms enable insurers to become more equitable and more customer-centric, with a fair assessment of every individual based on their unique health conditions and risks. These technologies are at the forefront to resolve several challenges fraught in the insurance sector across the country and globally as well.

Large volumes and severity of insurance claims cause longer claim settlement times. On the other hand, disintegrated, manual operations hinder efficient claim investigations and increase claim settlement costs and loss ratios. These challenges withhold insurance companies from increasing their margins and market shares. AI in insurance addresses these concerns by assisting data teams in simplifying data collection, automating key processes, and therefore, optimising business decisions and marketing spending. This empowers insurance companies to identify profitable customer segments and offer seamless and exceptional customer service at all times, thereby growing their revenues.

Digitising insurance end-to-end, for greater personalisation and profitability

By engaging advanced AI, Machine Learning (ML) models, insurance carriers can ensure improved predictive accuracy of individual consumer behaviour and historic trends. This drives an accelerated understanding of customer segments and provides carriers with a deeper insight into insurance claim patterns in real time.

Leveraging accurate AI technologies, especially for ‘high payout – lower premium’ products like life insurance, enables companies to carefully screen and classify prospective applicants who they can insure, conduct precise risk assessments and deliver risk-based pricing recommendations with ease, for every individual.

This ensures that end-to-end insurance processes are more flexible and go beyond conventional, rules-based policies, ultimately leading to higher conversions, better underwriting, and efficient claims management. Globally, companies are already using AI-driven chatbots for customer onboarding and engagement processes, along with automated claims resolution with minimised need for human involvement from start to end. These automated chatbot interactions with customers across platforms, generate a rich database of customer insights, enabling analysts to continuously review and improve future customer experiences.

Enhanced decision-making and customer experience at every stage

Modern AI and ML tools equip insurers to take prompt and action-driven decisions that mitigate loss due to fraud and policy lapse risks. By assisting renewals teams to supervise potential policy lapsations and their causes, which are otherwise very difficult to identify, these tools offer a mechanism to prevent lapsations at individual customer levels. In this way, AI-driven intelligence enables analysts to strategise toward providing superior customer services where they are most required. Data-driven decision-making also leads to a reduction in false positive rates and overall loss ratio, and an improvement in time to deployment.

Insurance processes that are backed by AI also lead to more effective targeting of customers based on demographics, psychographics, health records, etc. Furthermore, it ensures that the targeted customer audience can access custom quotes, policy details, and payment assistance almost instantly. These automated provisions significantly improve the retention of policyholders, and promote loyalty and customer acquisition, while reducing customer churn rates. With reduced administrative costs and improved customer experiences, revenue leakages can be controlled effectively and at early stages.

Behavioural economics facilitated by AI creates an opportunity for early intervention in claims handling as well, and this in turn accelerates the claim investigation and minimises overall turnaround time from assessment to settlement. Over time, improved claim outcomes generate higher confidence in the accuracy of claim approval decisions and straight-through claim processing abilities. Claims executives can optimise resources, and forecast claim volumes and severities in an improved and productive manner.

Tapping on the rise of digital-first insurance companies with AI and data intelligence

AI-based solutions successfully maximise the growth and scalability of insurance companies by delivering enhanced personalisation, data-driven cross-selling and up-selling suggestions, and improved marketing strategies. The power of AI and Big Data makes it possible to implement early prediction and prevention measures, adding critical value to an insurance lifecycle, across both non-bank and banking insurance companies. By providing ample time for investigation and effective risk management, AI-driven tools identify claim abuse and fraud scenarios in time, thereby nurturing an effective insurance ecosystem across all touch points including policyholders, advisors, and carriers.

In a competitive insurance industry like today’s, it is important for insurers to adopt technology-powered processes, and loss-controlling tools with risk-specific pricing models and also to identify early indicators of policy lapse risks. AI and ML technologies seamlessly fulfill these requirements and offer the opportunity for insurance as an industry to become fairer, faster and more affordable across customer segments. Insurance companies that embrace an innovative, AI-driven mindset that can effectively predict and deter, will thrive and succeed in the future of the insurance industry.

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Automating the data science/machine learning lifecycle to unleash AI’s full power

There’s little doubt that artificial intelligence (AI) will be core to the business model of every leading enterprise of the future. But right now, stories abound about how difficult it can be to unlock business value from big data and AI. Take the example of a financial institution that spent R1.5 million and six months to develop a customer attrition model, only for it to deteriorate in less than a year.

This company is by no means unusual. As we speak to financial institutions and other large businesses, they share stories about spending months and millions of rands developing AI models that fail to meet expectations or even to deliver any value. This is a global challenge, with one study finding that 87% of data science and machine learning (DSML) projects do not progress beyond prototype and R&D stage.

Suman Singh, founder and CEO of CyborgIntell, often speaks of his time in the data science trenches, where he learned first-hand why so many data science and AI projects fail. He led a team of 80 high-calibre data science experts – many of them educated at the most prestigious American universities – that struggled to gain the expected ROI from machine learning and data science projects.

Data scientists – overworked and under pressure

Even though they were working excessively long hours, these data scientists were constantly under pressure and falling behind schedule. More than 90% of projects were not delivering the expected benefits. Singh realised these extremely clever people were spending an undue amount of their time doing slow, repetitive, manual work.

That was the situation of a company that could afford to attract and retain a team made up of the best. It’s even more difficult for companies in South Africa competing for a small pool of talent. Building a team that spans the DSML lifecycle is costly and complicated. Outsourcing comes with its own challenges such as intellectual property ownership.

Manually carrying out tasks such as data selection and modelling or operationalising AI is not only mind-numbingly dull for the data science and AI team. It’s also slow, expensive and doesn’t scale. The result is it takes so long to develop, deploy and operationalise ML models that the data the system was trained on is often out of date before it is ready to be deployed.

The solution that Singh came up with was to elevate the level of automation in the DSML lifecycle. Companies today can benefit from a new approach to AI, based on a one-stop, zero-code method for rapidly developing, deploying and operationalising AI applications at scale. This approach slices the time to deploying AI projects from many months to as little as two to four weeks, while helping to reduce risks and enhance ROI.

A 250-fold productivity boost

Automation is up to 250 times faster than manual approaches and eliminates human error from the equation. Where a team of data scientists could build dozens of machine learning models, automation enables them to scale up to millions of models if they wish, improving accuracy. DSML models can be deployed in seconds, while auto retraining, validation and redeployment can be accomplished in hours rather than days or months.

Such a solution reduces the time required to develop accurate, production-ready models to a few hours without writing any code. A scalable AI platform can address a variety of use cases for every enterprise in various industries, from optimising pricing, driving cross-selling and upselling opportunities, and targeted marketing to predicting loan defaults, pricing insurance risks, automating claims approvals, and detecting fraud risks.

For companies in regulated sectors, issues of risk, trust and governance are high on the agenda. They need to be able to explain how an algorithm decides someone is a fraud risk or why a loan application was refused. Today’s solutions enable a company to interpret, explain, and trust ML models. They mitigate bias and manage risk.

The promise of getting a working AI system up and running within weeks is a game-changer for AI. But just as importantly, introducing an automated DSML lifecycle enables the business to democratise AI and put this powerful tool in the hands of more people, including business users. That will be the key to accelerating AI adoption and unleashing its full value in the years to come.

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Indian AI company CyborgIntell opens office in South Africa

CyborgIntell, an AI platform company from India, has opened an office in South Africa. Headed by Bryan McLachlan as MD, who has over 30 years' experience in financial services, CyborgIntell Africa will work closely with financial institutions and other enterprises.

McLachlan says: “We are excited to be investing in Africa with a view to democratising AI and helping organisations unleash their full power. Our solution reduces the time required to develop accurate, production-ready models, empowering business users to deploy their own data science projects.

"Furthermore, by eliminating much of the manual work that used to go into developing, deploying, and managing algorithms and data models, we can help companies slash time and improve efficiencies in AI and data science programmes."

CyborgIntell was founded in 2018 in Bengaluru, India, by Suman Singh, Amit Kumar and Mohammed Nawas. The CyborgIntell platform addresses the key challenges companies face in the data science/machine learning lifecycle – from data selection and modelling, operationalising AI, to managing risk and governance.

"AI is a powerful and transformative technology, yet many companies across the world find it difficult to unlock its full potential. More than a third (36%) of organisations take more than 90 days to deploy data science machine learning (ML) projects, while the failure rate of such initiatives is estimated to be 85% across industries."

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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.

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.

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?”

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.

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.

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.

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.

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.

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.

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Sense AI, Penthalon Ventures Back SaaS Platform CyborgIntell

Artificial intelligence-backed software platform CyborgIntell said it has bagged a funding of $1.19 million (around Rs 8 crore) from SenseAI and Pentathlon Ventures, along with Ghosal Ventures’ participation.

The company plans to use the fresh capital to bolster its teams across sales, marketing as well as product research and development. It also aims to scale North America operations, onboard more customers and expand India and South Africa’s markets reach. Launched in 2018 by Suman Singh, Amit Kumar and Mohammed Nawas, CyborgIntell claims to automate entire lifecycle of data science and machine learning, to boost the data-to-decision cycles for businesses. Its partner networks include HPE, NewGen, TempleGate Technology, AxionConnect, among others.

The support from SenseAI, Pentathlon, Ghosal Ventures and all our existing investors will boost our growth plans. It will help us generate higher value for our customers with more innovative and ground-breaking products and solutions.

Our mission is to unlock the potential of data and empower enterprises to become highly data & predictive intelligence-driven," said Suman Singh, Founder and CEO, CyborgIntell.

CyborgIntell's flagship product iTuring is a no-code AI-driven data science and machine learning software that enables enterprises to develop, deploy, operationalise and manage the risk of sophisticated machine learning models on a single platform.

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LoanTap chooses CyborgIntell’s AI Platform to accelerate digital lending solutions

LoanTap announced its collaboration with Cyborgintell, an AI software company. LoanTap has always maintained its brand image around providing flexible and friendly products for their consumers. Through this collaboration, LoanTap aims at simplifying their overall journey and introduce a unique AI model in the digital lending space. The model will help develop and on-board quick AI solutions for better consumer service and minimize operational costs.

The inclusion of AI and ML algorithms will enable LoanTap to assess large quantities of consumer data, analyze consumer behavior, organize, and track consumer journey to prevent defaults, therefore improving the turn-around time for loan disbursals. AI will further guide the consumers throughout the loan journey, including reminders to repay timely, and develop consumer profiles which will eventually lead to us being able to service our consumers efficiently along with offering them an ideal product.

Gautam Sinha, the Vice-President-Technology of LoanTap said, “At LoanTap, we believe innovation is at our core, we have always aimed at re-inventing the financial dynamics, introduced fast loan processes for the convenience of our consumers. This collaboration is a step forward in that direction, wherein we are aiming at financial inclusion. The adaptation of artificial intelligence will enable us to target our consumers and make internal processing of loans seamless, ultimately driving value for our customers with a customized, fast, and convenient experience”.

*Suman Singh, the Founder, and CEO of CyborgIntell adds,”we are very excited to join hands with LoanTap and be a part of their success journey in helping them to be the new-edge AI-first lending company to source quality customers, offer instant loan decisions, and early warning indicators for loan default management. AI-First strategy is going to reduce customer acquisition cost significantly, reduce risk exposure, enhance revenue and improve their customer experience.”

The AI models will help LoanTap understand various consumer personas including salaried and self-employed. It can further perform customer segmentation based on their needs, develop consumer profiles which will eventually enable LoanTap to service their consumers efficiently along with offering them an ideal product.

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A symbiotic relationship: Startups give even as they take

“Digital transformation is central to every large enterprise’s core strategy,” says Madhurima Agarwal, director and leader, NetApp Excellerator. “As large enterprises make extensive investments there is a clear preference for use of Big Data, analytics and Artificial Intelligence (AI)/Machine Learning (ML) to protect existing business and find new growth strategies. Startups have proven to be a key enabler of digitalisation goals of large enterprises.”

A recent Zinnov-Netapp report suggests that by 2023, global digital engineering and R&D spends will touch $750 billion, with startups and tech giants as key drivers.

Currently, startups cover a range of capabilities from data analytics to AI in the digital world. As per the report, in the BFSI value chain alone, 90% of AI infusion use cases that startups are involved in are across operations, products and services. Several operate on the sales and marketing sides as well where the major chunk of the work is digital and metric driven. Similarly, mobility, healthcare, logistics and manufacturing industries have strong AI infusion. “The Indian B2B technology startup ecosystem is growing rapidly. Not only do they constitute 44% of the total base, 43% of all Indian unicorns are B2B. The fact that three of seven unicorns in 2020 (YTD) are B2B startups reflects the rapidly maturing ecosystem,” says Agarwal.

A good digital infrastructure and a collaboration model between large enterprises and startups stand as key pillars for this growth. New-age infrastructure with hybrid clouds and powerful cloud-based processors play a major role in breaking the data silos. As a result, various use cases involving various data points could be efficiently brought together. While CIOs, CTOs are constantly working towards bettering this, the business folks around the world are experimenting with the collaboration models.

“We partner with startups to create joint offerings. We recently published a whitepaper with Curl Analytics. One of its solutions, Paras, an automated ML engine, was deployed at our AI Centre of Excellence in the Bengaluru campus. The integration of our data management software ONTAP with the Paras AI solution unlocked an end-to-end AI value chain for users, resulting in cost savings of above 80% over the cost of standard AI development and execution,” she adds.

Platform evangelisation, license or vendor agreement, joint go-to-market, co-innovate, equity investment and acquisitions are most popular. According to the report, ecosystem outreach, accelerator, partner program, corporate VC, mergers and acquisitions are the ways that technology corporations approach the above models. In 2019, there were 170+ unique corporates (12-15 y-o-y increase), 90+ investments (up 15% from 2018), 40+ mergers & acquisitions and over 60 open innovations, says Aggarwal.

The startup-corporate relationship needs several iterations to make it a suc- cessful partnership. Both the parties are on a steep learning curve, discovering hidden potential. “Large corporations tend to be complex and most of the times, departments operate in silos. With scarce budgets spread across multiple functions, it is challenging to paint the bigger picture that can drive ROI. Our strength has been the ability to engage across the enterprise to deliver an upfront ROI in just a couple of days,” says Suman Singh, CEO, CyborgIntell.

Enterprise leaders today expect the same speed of execution and reliability as what they experience as individual consumers, says Praphul Chandra, founder, KoineArth. “The appetite for custom projects which require large capital expenditure and months to deploy is low. Enterprises want ready-to-use solutions for which they can pay as they go. This is where they look at startups,” he says.

Different paces of working is a major pain point when two organisations of different sizes partner. According to Agarwal, a dedicated team with experience across both large organisations and startups can best resolve this.

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Data-driven decision-making through Artificial Intelligence (AI) and Machine Learning (ML) involves leveraging algorithms and models to analyze large volumes of data and extract meaningful insights.

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THE DEMO DAY OF THE SIXTH COHORT OF NETAPP EXCELLERATOR SHOWS WHAT IT TAKES FOR STARTUPS TO GROW BETTER, FASTER AND STRONGER

The Demo Day of the sixth cohort of NetApp Excellerator, NetApp’s flagship startup accelerator programme, embraced a new virtual route. The accelerator had earlier pivoted into a virtual programme for its sixth cohort, wherein the selections, workshops, coaching sessions, mentoring sessions and PoCs took place entirely online.

Madhurima added that the feedback they received from the startups was uniform - at no point did they feel things were not business as usual. Of pivots, exits and launches In her address, Madhurima also shared how startups of the current cohort as well as alumni developed new offerings in the light of the pandemic. For instance, Securely Share has developed a solution for sharing of data securely during the work from home base; Cardiotrack has developed an offering for enterprises to manage their workforce who are getting back to work and Senseforth.ai which is working with international organisations like NHS, is making a meaningful impact for thousands of patients. Moreover, startups like Cyborgintell, Koinearth, Curl Analytics, EDER Labs and AiKaan Labs are also working on some fantastic offerings in Supply chain, AI, ML and IoT.

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Cloud And AI Innovations Dominated At The NetApp Excellerator Demo Day

NetApp held its sixth demo day for the NetApp Excellerator, the company’s flagship startup accelerator program. Embracing a new virtual world, the demo day was held via video with the six participating startups and guests from across the globe. With the sixth cohort graduating, 35 startups are now part of this unique startup accelerator program that is focused on cloud and data related technologies.

NetApp responded to the COVID lockdown with a quick transition to an online curriculum and selected six deep tech startups, Aikaan Labs, Cyborgintell, IQLECT, Koinearth, Kubesafe, and Myelin Foundry, for the sixth cohort, virtually. The startups showcased business-critical solutions using Artificial Intelligence, Kubernetes, machine learning, IoT and blockchain.

Through a four-month remote networking and mentoring period, the startups strengthened their business during a global crisis situation. Inspired by this opportunity, they even contributed to finding solutions in navigating this tough time. For instance, Myelin Foundry, a deep tech AI start-up that develops AI algorithms on video, voice, and sensor data for edge devices, is revolutionizing the video streaming space, which is seeing a huge uptake during the pandemic. Similarly, AiKaan Labs, a startup that provides a deep view into edge computing and IoT devices, is helping accelerate digital adoption.

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Customer Centric Retention in Retail Banking

The easiest way to grow revenue is to keep your existing customers happy. It can cost up to five times as much to acquire a new customer, as it does to retain an existing one. Nurturing your existing customers, yields far better sales results with a success rate of 60-70% selling to existing customers compared to 5-20% selling to a new customer.

Artificial Intelligence (AI) plays a significant role in transforming the retail banking industry, enhancing efficiency, customer experience, and decision-making processes.

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The Evolution of Data Science

The Evolution of AI & ML to make data driven decisions and solve business problems using innovative AI systems. Learn how Automated Data Science (AutoML+ MLOps) can help you make accurate data driven decisions in a couple of days. AI applications are a common place in most industries to help businesses automate, predict, optimize, and innovate. AI and ML are used widely across the world to solve a variety of problems – face and speech recognition, medical diagnosis, self-driven cars, and even robot pets.