“Equipped AI Helps Alternative Investors Harness the Full Potential of Their Data” Says Atul Arora

Instinct and gut feeling determine the course of most trading and investment decisions. Nevertheless, with the availability of a huge swathe of data, it gets very alluring to depend on numbers for stunning results — only the necessary ways and means are accessible to derive useful insights from the random numbers. Equipped AI, a global analytical intelligence and software company, combines data science and technology to provide actionable insights into its business portfolio. Analytics Insight has engaged in an exclusive interview with Atul Arora, global COO of Equipped AI.

  1. Kindly brief us about Equipped and the products and services that it offers.

Equipped AI is a B2B fintech that provides solutions for alternative investment managers to enhance their deal-flow management and data management. The company has its headquarters and sales office in the UK and its operations in India.

Equipped AI started as an in-house technology solutions division of AnaCap Financial Partners, a leading investment house in the UK. In 2021, the division was spun off as a separate company.

Our solutions are divided into three distinct categories. Minerva, the flagship deal flow management solution from Equipped, is an industry-first end-to-end deal flow management system that caters to the requirements of the alternative investment management industry.

Equipped also offers a state-of-the-art Data Management solution that covers the data lifecycle from ingestion to visualisation to provide investment managers with a more granular view of their investments.

Equipped also offers professional services for the buy-side including analytics, investment research, and business research services to support asset managers who need greater analytical rigour in analysis and a deeper understanding of the markets.

  1. Who are your customers? Where are they based?

Today, Equipped AI is the world’s leading technology-based analytical intelligence solutions and advisory services provider for alternative investors. The company works with a broad spectrum of alternative investors and their portfolio companies to structure and cleanse their data inputs. A significant majority of our customers are based in Europe and North America.

A few of the leading clients we work with are Amitra, Quilam Capital, RCapital, Milleis Banque, Dansk Sundhedssikring, AFE Asset Management, Novia Financial, Nest Bank, Veld Capital, Belasko, and Blazehill. Since we also offer workflow software tools to streamline communication, dashboard assets and build automated reporting packs, our customers are anyone from Private Equity and Private Credit firms to Real Estate Funds, and even portfolio companies of Individual firms spread across the globe.

  1. What is the value proposition that Equipped offers to its customers?

Despite investing and managing trillions of dollars in assets every year, the global alternative investment industry has been remarkably hesitant to adopt and use modern tech tools for its workflows and processes, including deal flow management and portfolio tracking. Most data is stored, managed, and analysed in excel sheets or personal diaries, preventing these firms from taking advantage of advanced analytics to enhance value creation.

This is despite the availability of cutting-edge software from several vendors in the market for years, all of which promise to bring all the advantages and efficiencies of automated workflows and processes that only a specialised software vendor with domain understanding can offer. Since most of our peers typically address only one or two aspects of the entire deal process, it has the side effect of making it more cumbersome to use. Additionally, several of these software tools are designed with little understanding of how the investment industry works, creating further barriers to their wider adoption.

As a company that is borne out of the alternative asset management industry, Equipped AI has leveraged its extensive experience in the industry to create software that is much more intuitive and yet powerful than what is available with other tech companies. Equipped has worked extensively with asset and investment managers to structure and automate the continuous input of data from disparate sources including portfolio companies, analysts’ models, licensed data, and PDFs.

Equipped’s flagship asset management software (Minerva) enables investors to collect, compile, analyse and better understand the data effectively and efficiently. The data then helps investors to focus on decision-making rather than on data entry and in turn produce superior returns. Currently, Minerva hosts over 75,000 individual assets worth over GBP£25 billion.

  1. Would you like to highlight a few use cases where data analytics has benefitted your clients tremendously?

In the past three years, we have encountered numerous scenarios to qualify data analytics as a benefit to clients.  For instance, we recently worked with a pan-European granular credit investor with strategies across performing (PL) and non-performing loans (NPL), consumer, SME and corporate debt. This client also has exposure to structured credit and real estate.

The team informed us that they faced difficulty in identifying the drivers of performance within their PL and NPL portfolios. They also struggled to monitor, manage, and challenge the operational activities of servicing and operating partners. Finally, they had no way to quickly identify outliers and the evolution of portfolios.

To overcome these challenges, we designed and created a suite of interactive, deep-dive portfolio monitoring dashboards and operational tracking tools. We also surfaced the drivers of portfolio performance and correlated these directly with servicing and operating partners’ activity. Additionally, we derived a new collection strategy from data cleansing analysis that optimised performance and enabled swift, proactive intervention.

Data analytics from Equipped thus helped the client access timely, accurate, and meaningful performance metrics surfacing with the touch of a button. The asset management team was able to drill down into key portfolio segments to understand emerging trends, breakages in payment plans and other deviations from core assumptions. Lastly, automatic identification of outlier cases enabled the client to productively engage with servicing and operating partners, revise tactics and ultimately drive incremental collections.

  1. How can businesses efficiently extract value from their data without increasing cost and complexity?

Equipped to deploy data science and advanced Analytics to help alternative investors harness the full potential of their data. We cleanse, reconcile and structure client data from disparate sources and build structured data warehouses. Here, we extensively use technology and data science to solve real business problems, surface actionable insights and forecast future outcomes.

Technology has been the building block for us to extract data without increasing costs. Our data engineers and scientists are experts at converting raw, disparate data into centralised strategic assets. The reconciled and structured asset can then be leveraged to create a competitive advantage, drive operational efficiency, and improve investment performance.

  1. How has been your journey in the analytics industry and what are some of the analytic solutions that you have worked on?

Analytics and data offer structure to chaos and helps make sense of the plethora of data that we come across on a daily basis. The 20 year long journey in data processing and analytics has been filled with immense learning, a lot of creative solution building, and sometimes  painful research/exploration but overall full of a sense of achievement and extremely gratifying

            My journey with data started with regulatory reporting (pre-SAS days when reporting tools weren’t as popular), went on to building scorecards for campaigns, fraud detection through outlier detection techniques as well as behavioural modelling, optimising allocation and cost using complex applications of linear programming. Most of the use cases I have dealt with come from the Financial Services domain; however, I have also done some projects in the retail space and with the Ministry of Women and Child Development, that have been among the most impactful implementations of analytics in my journey.

  1. Is the right kind of talent a challenge in the industry?

Finding the right talent with desired skills and attitudes is a universal challenge for all companies developing and working on cutting-edge technologies. It is much harder in ours. We work with a limited pool of prospective candidates who come with knowledge and understanding of both technology and the alternative investment industry. It is also imperative to find people who are keeping abreast with the ever-evolving technology and changing financial regulations. To add to this, the competitive nature of the industry is also at play. To address these challenges, we have prioritised building a great work culture that enables our people to do their best work every day. Still, we are a fast-growing company and are thus always on the lookout for great talent.

  1. What would you advise aspiring big data and analytics candidates?

Big Data and Analytics are both highly promising and rewarding career options and to make a mark in these domains, candidates ought to first focus on mastering the fundamentals, while practising their skills by solving problems using actual datasets. One of the most helpful recommendations for aspiring data analytics professionals is to learn to work like a data engineer too.  Another thing that helps solve business problems using analytics is domain/commercial knowledge. I always advise data scientists to first understand the data and business context before jumping into slicing and dicing the same and applying mathematical techniques. Inferences of the outputs or modelling become so much more meaningful once you understand the data and business context well. It also helps explain the model output to the business leading to better interpretability and thereby, adoption.

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