{"id":17158,"date":"2023-10-12T07:14:55","date_gmt":"2023-10-12T07:14:55","guid":{"rendered":"https:\/\/mysourcefunding.com\/leadership\/ntropy-aims-to-make-financial-services-cheaper-and-more-accessible\/"},"modified":"2023-10-12T07:14:56","modified_gmt":"2023-10-12T07:14:56","slug":"ntropy-aims-to-make-financial-services-cheaper-and-more-accessible","status":"publish","type":"post","link":"https:\/\/mysourcefunding.com\/?p=17158","title":{"rendered":"Ntropy Aims To Make Financial Services Cheaper And More Accessible."},"content":{"rendered":"
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Financial institutions spend a lot of money on the data and analytics needed to assess the creditworthiness of their customers. In fact, the global financial analytics market was valued at $7.6 billion in 2020, and is projected to reach $19.8 billion by 2030, according to Allied Market Research.<\/p>\n

The cost and unavailability of credit data make financial institutions overly conservative, often denying services to otherwise potentially valuable customers, especially small and medium sized businesses.<\/p>\n

Nar\u00e9 Vardanyan and Ilia Zintchenko wanted to change that dynamic by making data cheaper and more accessible with the founding of Ntropy in 2018. This founder\u2019s journey is based on my interview with Ntropy CEO Vardanyan.<\/p>\n

\u201cWe started Ntropy to build a unified data layer across all use cases, all types of financial institutions, almost like a consortium where you could input any type of financial data, and then get an understanding what is happening, who is paying who and what is the context. And if you have those basic things, you could accelerate a lot of the processes and financial services and make them cheaper,\u201d says Vardanyan.<\/p>\n

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The name for the company was chosen by CTO Zintchenko, who has a PhD in Theoretical Physics, according to Vardanyan. While \u201centropy\u201d is a term used to describe the second law of thermodynamics, the word itself is derived from the Greek word entropia, (\u03b5\u03bd\u03c4\u03c1\u03bf\u03c0\u03af\u03b1), which means a \u201ctransformation,\u201d which is more descriptive of what Vardanyan and Zintchenko are accomplishing with Ntropy. They want to transform the way financial services use customer data.<\/p>\n

Ntropy positions itself as the most accurate financial data standardization and enrichment API, which uses Large Language Models (LLM) to be able to reconcile any data source. \u201cWe could immediately show how our system works. Within milliseconds, the API just parses and cleans the data. So, there was like that magic effect with the product,\u201d says Vardanyan. Banks and financial institutions often have the data they need, but its often not in a usable format, because they don\u2019t have the software skills to do so. Many financial institutions send their data to organizations like credit bureaus who then format the data into a usable report and then sell it back to them at a large expense.<\/p>\n

As the company was getting started and were working to perfect their algorithm, the co-founders had a conversation with QED co-founder and managing partner Nigel Morris who started Capital One and revolutionised banking industry. \u201cThey got very interested, and they had a portfolio of companies from Credit Karma to and others who are changing financial services in the United States and beyond. And they would all be potential customers of our very simple service, which would take raw sort of bad data, and then make it easier to use. And that’s, that’s how we got started in January 2021, when we raised our first round of funding,\u201d says Vardanyan.<\/p>\n

Today, the 25-person Ntropy has an engine that understands financial data of any format or language. The company currently supports over 10 languages and a growing list of different currencies all one system that can be digested in order to create clean information to build financial products and services.<\/p>\n

\u201cWe’re still at the beginning because we’re a pretty small company, but we went from 20,000 transactions to doing hundreds of millions of transactions a month that go through the system right now. We have a database of 100 million plus unique merchants, which is unique businesses that we have seen before,\u201d says Vardanyan.<\/p>\n

Instead of having hundreds of humans doing hundreds of tasks and reading the data, having a machine do it is cheaper and faster. Vardanyan believes that as access to the data improves, many more people can have their business underwritten in a shorter period of time, many more businesses can access capital without increasing the risk. \u201cThings can be audited faster as well. So, you basically open up access by making intelligence cheaper. And that’s what we’re trying to do,\u201d says Vardanyan.<\/p>\n

The company has grown 400% over the past year, with hundreds of customers in the US working with Plaid, Ocrolus, Yapily and other financial data companies. They also have partnerships in Latin American in Brazil and Columbia and are moving into the Middle East. As a result, the NY-based financial data intelligence platform provider has raised $14 million to date. It\u2019s latest $11 million Series A funding in October of 2022 was led by Lakestar with support from existing investors, QED Investors and January Ventures, as well as angel investors including executives from Twilio, Ramp, MoonPay and ComplyAdvantage.<\/p>\n

Vardanyan was born and raised in Armenia, a small country in the southern Caucasus, with a history of turmoil. \u201cI left when I was 17. I was working with the United Nations, part of the young professionals program. I was mostly involved in the financial inclusion piece. So, I spent some time in New York City and then moved to Geneva to the European office,\u201d says Vardanyan.<\/p>\n

After two and a half years, she realised that she didn’t want to stay in that world because it was extremely bureaucratic. She wanted to solve real problems and knew that data had a huge role to play. She knew she would need an understanding of how data works and AI. So, she went to the UK and earned her degree from the University College London, where they had an advanced AI program, teaching computers how to play video games, for example.<\/p>\n

She started her first company because it was the only way she could retain her visa. \u201cIt was a stage where I didn’t have much to lose, and I just decided to take it on. I started the company with a friend of mine from UCL engineering to solve the problem of detecting early stages of mental disorders from phone data. We decided to sell the IP of the business to Mercer, so I learned a lot,\u201d says Vardanyan.<\/p>\n

After that successful exit, she started working with a venture fund that had invested in her first company. \u201cWe started an AI first fund doing AI and machine learning deals into early-stage companies. And two years into that I realised that I wanted to be on the other side again,\u201d says Vardanyan.<\/p>\n

\u201cI was thinking that from my UN experience and from just growing up in a third world country with a very different system around money, financial data is one of the most important datasets in the world. What we spend on and how we spend is crucial to many things from payments, remittances, loans, mortgages, and how we interact with the economy and get access to goods and services. And my naive understanding was that that data is underutilised,\u201d says Vardanyan who joined with Zintchenko to launch Ntropy.<\/p>\n

As for the future? \u201cWe want to be at the back end of as many old and new financial products. Scaling the API to as many companies and as many people as possible is super important because that’s the only way we can see impact. If, let’s say we work with a lender today who serves 100 SMEs, that’s fantastic. If they get to a million, that’s even greater, because now we made a difference in lives of 1 million small business owners,\u201d concludes Vardanyan.<\/p>\n<\/div>\n

Read the full article here<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Financial institutions spend a lot of money on the data and analytics needed to assess the creditworthiness of their customers. In fact, the global financial analytics market was valued at $7.6 billion in 2020, and is projected to reach $19.8 billion by 2030, according to Allied Market Research. The cost and unavailability of credit data […]<\/p>\n","protected":false},"author":1,"featured_media":17159,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[76],"tags":[],"class_list":{"0":"post-17158","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-leadership"},"yoast_head":"\nNtropy Aims To Make Financial Services Cheaper And More Accessible. | Brandiary<\/title>\n<meta name=\"description\" content=\"Financial institutions spend a lot of money on the data and analytics needed to assess the creditworthiness of their customers. 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