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How G-Square Brought In A Bot And Plug-And-Play Element Into Analytics

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How G-Square Brought In A Bot And Plug-And-Play Element Into Analytics

Key takeaways

  • G-Square Solutions focuses on replacing manual data analysis with Robo-analytics products, primarily for the financial services sector.
  • The company aims to provide plug-and-play analytics, addressing a gap in the market for accessible data analytics solutions.
  • G-Square operates with a B2B approach, offering SaaS-based financial products tailored to client needs in sales and risk analytics.
  • Founders Gurpreet Singh and Gopi Suvanam identified the under-penetration of analytics products as a significant opportunity in 2014.

Takeaways drafted with AI from the article text.

G-Square founders Gurpreet Singh (L) and Gopi Suvanam.
G-Square founders Gurpreet Singh (L) and Gopi Suvanam.
  Artificial Intelligence and machine learning are transforming the way the industry is dealing with data analytics solutions. The key to success for most organisations today is data-driven decisions. Mumbai-based G-Square Solutions aims to replace manual data analysis with Robo-analytics products, especially for financial services, fin-tech companies and aggregators. There are very few global players who provide plug-and-play analytics and that is exactly what triggered founders Gurpreet Singh and Gopi Suvanam, to harness the huge potential and under-penetration of analytics products and services in 2014.

The underlying technologies:

In an exclusive interview with Analytics India Magazine, Gurpreet Singh, Director and Founder of the company said that G-Square Solutions works in the data analytics and Business Intelligence space primarily for the financial sector. The company works both in the retail sales analytics and risk analytics side through their products and services largely focused on products.   G-Square has a B2B approach whereby it offers SaaS-based financial products and also provides services in sales, marketing and risk analytics tailored to the client requirements. Other areas where it provides its tools and services are across Banks, NBFCs, Wealth Management Companies, Asset Management Companies. With a strong expertise in Artificial Intelligence, Machine learning, the company aims to apply strong data engineering in each assignment. Their uniqueness lies in using their in-house technology and proprietary algorithms for building models along with conventional methods “Amongst the problems we have solved through services, we have been an analytics partner for one of the largest financial distributor of the country and helping them focus on the customer and sales channel analytics on an ongoing basis worked with three banks working on propensity modelling and credit risk analytics. Similarly, we have worked with three Fin-tech companies, one NBFC and two asset management cos in enhancing sales through our analytics services and products too,” said Singh citing a few example of how the company is providing services.

The other deliverables:

The company works on other deliverables too: Retail analytics proposition: It works in the data analytics & Business Intelligence space for the sales & marketing teams to help achieve better results in top-line & bottom-line. The products in this space are Narrator, which is a Robo descriptive & prescriptive analytics tool; Clientrator, a customer predictive analytics problems; and Bigdator, an unstructured data analytics tool. Risk analytics: G-Square also works in the Market Risk and Credit Risk analytics streams to manage risk for the Treasury & Risk teams through our products & services. The Market risk products are Derivator and Riskrator. Derivator, as Singh explains, is a system for derivative risk and pricing, assessment of derivative risk, hedging metrics and hedge effectiveness. While, Riskrator is a system for identification, measurement & management of Underlying Market Risk for Interest Rates/Currency/Equity, Assessment and Active Management of exposure, VaR and Limit Management. “In our credit risk services, we work with Banks, NBFCs and payment companies in this space on building credit scoring models, Early warning signals and model validation etc.,” Singh said.

The G-Square journey and team:

Like most start-ups, Singh and Suvanam’s journey has been a roller-coaster ride too. In just three years, the company has three well-developed products and two other products which are in decent shape. The duo, who know each other since 2009, knew they complement each other in analytics, technology, marketing and business strategy, exactly the skills essentially required for the success of any enterprise. “Our operating team comprises of young nimble and energetic data scientists and technology associates with experience in analytics and expertise in product development. Initially, we tried our services and product ideas with some of our close contact network Clients and got very good response. Our services soon got good results and started getting fructified into the ideas of products which we had in mind,” said Singh. With its products and services, G-Square is dedicated to enhancing its capability in predictive and unstructured data products to make itself relevant and contextual to solve the respective business problems. Moreover, G-Square’s ambitions are to become a significant global product player in the plug-and-play data analytics products space, Singh told AIM. Despite the achievements the company has seen in the last three years, no success story is real without the struggles it faces during the initial days. The founders of G-Square confess of having no track record in B2B products when they started. Singh said that the enterprise analytics space is quite challenging in terms of longer sales process cycles and acceptability of products coming from a start-up is the other big challenge they overcame in order to survive and thrive. Giving industry insights, and explaining how the analytics industry has evolved in India in terms of adoption in AI, ML or big data, Singh said: “Firms are moving away from descriptive analytics towards actionable insights and Machine Learning-based automated analytics. What it means is that in the near future algorithms will be able to automatically derive insights out of data and take actions upon that with minimal human intervention.”

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Corrections: AIM corrects errors of fact and notes the change on the story. Spotted one? Tell the editors. Read our editorial standards.

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