Aditya Arora’s Post

From saying himself that he would never start a business to building one of the finest AI company in India. From pooling 2 lakh Rs. in 2000 to a 2000 crores revenue generating company in 2023 with a valuation of more than 1.5 billion dollars. From building the first 30 minute loan product for ICICI to building some of the latest and greatest AI companies that are empowering decision making for the fortune listed companies. Fractal is a company whose growth strategy, and forward thinking leadership need to be penned down in a carefully crafted business book and made available to all the business & tech students in India. Srikanth Velamakanni and Pranay Agrawal 's 23+ years tenacious partnership deserves a huge applaud and is something that needs to be looked upon by every budding founders. In a world where AI strategy is something which every other company is striving for and is treating it as a trade secret. Fractal with its Fractal Analytics Academy is teaching the Leadership Strategies for AI and Generative AI adoption, where the Group CEO teaches about his favorite subject "Human Decision Making and its Biases" and the CEO teaches "Successful AI Strategies: A CEO's Perspective". Fractal is a one of the finest example of a Responsible AI company and, they actually worked along with NASSCOM to create a responsible AI framework for India. Thanks a lot Srikanth for not sticking to your decision of "never starting a business" Thanks a lot Aditya Arora for picking it. #Fractal #Abusinessstoryworthwriting #businessbookweneed

🤝 Meet the 🇮🇳 man who built a 7500 CR AI company after his co-founders left 🫱 1. Born in the small town of Duliajan in Assam, Srikanth Velamakanni had made everyone proud. He studied Electrical engineering from IIT Delhi. Shortly, Srikanth started working at ICICI in their structured products department in Mumbai. But he noticed something. 🤔 2. ICICI relied on a US data company, Fair Issac, for their credit models. However, Fair Isaac did not have data to build India-based models. Srikanth thought he could solve this and left his job to start a data analytics company. In February 2000, Fractal Analytics was born. 🚀 3. Tieing up with five other classmates, Srikanth built a 32-page white paper for a mathematical model after four weeks. ICICI liked it so much that they started recommending Fractal to every bank. After 18 months, Fractal got its first big client. 💪 4. Fractal became the base layer for HDFC's risk management engines powered through predictive mathematical models. Everything was going well, but people did not pay for data in India then. Srikanth had to go to the US. 5. In 2005, Fractal moved to the US and scaled to 10 CR in revenue by next year. Srikanth became the CEO among five founders. And then June 2007 arrived. He was sitting in Bangalore when three emails popped up suddenly - Three co-founders wanted to resign. 🤯 6. Srikanth rushed to Bombay, and a dozen other resignations were waiting for him. They did not want him as CEO and sided with the warring co-founders to join a rival company. The global financial crisis of 2008 added to the misery. Srikanth and his other co-founder(Pranay Aggarwal) decided not to give up on their dreams and fight. 🙌 7. Srikanth launched the flagship customer genomics solution, helping marketers learn complex customer behaviour from transactions and social media interactions. 💵 8. By 2015, Fractal opened its second office in Bangalore and worked with clients like P&G, Kimberly Clark, Microsoft and Philips in 100 countries. On 11th May 2016, it raised 667 CR from Malaysia's Khazanah Nasional Berhad at a valuation of 2000 CR. 📉 9. It kept growing with the launch of Qure.ai (diagnostics) and acquisition of Final Mile in the behavioural architecture space. Growing at 40% yearly, It scaled to 710 CR in revenue by 2019 and raised 1420 CR from Apax Partners at a valuation of 3155 CR. 💰 But the BIG moment came on 5th January 2022. 👇 10.Fractal scaled to 1300 CR in revenue and worked with 100 Fortune 500 Companies. It raised 2700 CR from TPG Capital Asia at a valuation of 7500 CR. Fractal became India's second unicorn of 2022. 🦄 🌎 Srikanth Velamakanni has seen 21 years of struggle before building a unicorn. He is the poster boy for never giving up! 💪 #casestudy #🇮🇳 #realbharat #branding #growth #mindset #madeinindia #brandbluidling #heartwork #brand #genomics #Fractal #AI #digital Disclaimer : This case study 4524 is reposted only for educational purpose only

Went to IIT, Co-founders left him and still built a 7500 CR AI company. 1. Born in the small town of Duliajan in Assam, Srikanth Velamakanni had made everyone proud. He studied Electrical engineering from IIT Delhi. Shortly, Srikanth started working at ICICI in their structured products department in Mumbai. But he noticed something. 🤔 2. ICICI relied on a US data company, Fair Issac, for their credit models. However, they did not have data to build India-based models. Srikanth thought he could solve this and left his job to start a data analytics company. In February 2000, Fractal Analytics was born. 🚀 3. Tieing up with five other classmates, Srikanth built a 32-page white paper for a mathematical model after four weeks. ICICI liked it so much that they started recommending Fractal to every bank. After 18 months, Fractal got its first big client. 💪 4. Fractal became the base layer for HDFC's risk management engines powered through predictive mathematical models. Everything was going well, but people did not pay for data in India then. Srikanth had to go to the US. 5. In 2005, Fractal moved to the US and scaled to 10 CR in revenue by next year. Srikanth became the CEO among five founders. And then June 2007 arrived. He was sitting in Bangalore when three emails popped up suddenly - Three co-founders wanted to resign. 🤯 6. Srikanth rushed to Bombay, and a dozen other resignations were waiting for him. They did not want him as CEO and sided with the warring co-founders to join a rival company. The global financial crisis of 2008 added to the misery. Srikanth decided to fight. 🙌 7. He launched the flagship customer genomics solution, helping marketers learn complex customer behaviour from transactions and social media interactions. Fractal tripled its revenue with 200 clients in 2013 and received 150 CR from TA Associates. 💵 8. By 2015, Fractal opened its second office in Bangalore and worked with clients like P&G, Kimberly Clark, Microsoft and Philips in 100 countries. On 11th May 2016, it raised 667 CR from Malaysia's Khazanah Nasional Berhad at a valuation of 2000 CR. 📉 9. It kept growing with the launch of Qure AI (diagnostics) and acquisition of Final Mile in the behavioural architecture space. Growing at 40% yearly, It scaled to 710 CR in revenue by 2019 and raised 1420 CR from Apax Partners at a valuation of 3155 CR. 💰 10. And the BIG moment came on 5th January 2022. 👇 Fractal scaled to 1300 CR in revenue and worked with 100 Fortune 500 Companies. It raised 2700 CR from TPG Capital Asia at a valuation of 7500 CR. Fractal became India's second unicorn of 2022. 🦄 ➡️ Today, Fractal does revenue of 1985 CR with 194 CR in profits. It has over 4000 employees across 16 global locations. 🌎 Srikanth Velamakanni has seen 21 years of struggle before building a unicorn. He is the poster boy for never giving up! 💪 FOLLOW me for more Karan Barasara #startupstory #startup #cfbr

Recently, I had the opportunity to be interviewed by Flor Laorga for the AI Time Journal I have shared about: ↳ My Data Science Journey ↳ AI and Automation in Financial Services ↳ Impact on Financial Processes ↳ Innovative AI-Driven Solutions at Bajaj Finserv ↳ Challenges in AI Integration ↳ Future Work Evolution with AI and Automation ↳ Career Advice in Data Science and AI ------ → If you are just getting started, I have also shared the things you need to focus on. ------ → They're the skills that will be most demanded tomorrow. ------ ☑ DM if you have any use cases I could help you with! ☑ Always happy to help ------ ♻️ Repost if you like this post 👇 Comment below if you have any other questions 💡 Follow Belide Aakash for more on AI, data engineering, and making tech work for you! #AI #tech #finance #interview #datascience

The March 2024 edition of AIM Research Monthly is out now. In an era where data reigns supreme, India's BFSI sector is on the brink of a monumental shift, guided by visionary leaders who adeptly navigate the intricacies of technology, regulatory frameworks, and evolving consumer dynamics. These leaders, including Amar Kaul of Barclays, Anand K Sundaram of IDFC FIRST Bank, and Arun Santhanam of Chubb, are architects of change, tackling challenges of data security and digital transformation with resilience and innovation. Others like Kiran Samudrala of Equifax, Madhurima Khandelwal of American Express, and Mathangi Sri Ramachandran of Yubi, exemplify excellence in data strategy, analytics, and AI, driving their organizations towards a data-centric future. Mohan Sushantam of Vivriti Capital, RB Rajendar of Citi, Shubhosree Dasgupta of HSBC, and Shweta Singh of Tata AIA Life Insurance bring unique expertise and leadership to India's BFSI analytics landscape. This edition showcases the broad-reaching impact of Generative AI across industries. Udemy and Cognizant unveil Gen AI-powered initiatives for learning and software engineering, respectively. Deloitte enhances its AI capabilities through acquisition, while Kyndryl and Google Cloud expand responsible AI solutions. Capgemini collaborates with Mistral AI, Cisco introduces trustworthy AI deployment, and Pegasystems launches GenAI Blueprint. Additionally, EXL partners with Amazon Web Services (AWS) to drive Generative AI advancements, marking a significant moment in AI-driven progress. We also feature insights from Rishi Bhatia on Decoding Digital Fraud, Chet Kapoor of DataStax discussing the interplay of AI, Data Science, and modern data infrastructure, and Anirban Nandi exploring ethical considerations in Gen AI hallucinations. Vivek Jetley's insights on practical applications and future implications of Generative AI in today's business landscape provides valuable insights into the practical integration and future impact of Generative AI. Access the edition here: https://lnkd.in/g5H8NE9p Mastercard, Dolbey Itron, Inc. Amdocs

Milind Jadhav is a Lead Data Scientist in the AIML team at Fractal. He has a decade of experience solving business problems for Fortune 500 clients across multiple domains such as Insurance, banking, and Technology. With his expertise, Milind leads data science delivery of client engagements, creating automated decisioning solutions through the application of AI and machine learning. Analytics India Magazine caught up with Milind to understand the present state of the field and the potential it holds. Read the complete story to learn more about his data science journey 👇

#DataAIWorldTour is coming to Mumbai for the first time ever! Join hundreds of data and AI experts and industry leaders in shaping the future of #generativeAI Join to discover best practices across data engineering, data science, and advanced analytics on the lakehouse architecture during practitioner track. Register Now -> https://lnkd.in/dm7r2-wt #Databricks #Dataengineering #DataScience #Analytics #Lakehouse #AITransformation #Mlflow #Data #Automation #AI #Businessintelligence #Technology #Future #Bigdata #Cloudcomputing

Our customers and partners are familiar with the claim that the perfect model does not exist and that we at SEKASA Technologies Ltd. strongly believe in this claim. 🌟 Maybe it's even our internal company mantra 😉 but what's behind this claim? 🤔   (#Quant) analysts use a variety of models and approaches to process the diverse data that influences stock prices. These models exemplify the diverse set of tools available for handling macroeconomic indicators, industry-specific data, global economic factors, and company financials. Each model caters to different aspects of the data landscape, allowing investors to gain valuable insights and make informed decisions in response to changing market dynamics. 📊   The "best" model varies depending on how closely the modeling aligns with the market pricing of each factor. While some models offer advantages such as excellent interpretability or the ability to handle large datasets (as seen in Machine Learning models), the determination of the best model is contingent on its proximity to accurately capturing market pricing for a specific factor. Consequently, the „best“ model is subject to change based on evolving market conditions and the nature of the data being priced in. And a combination of many different models is always the key 📈   Today we will look at six examples that we use to some extent, but of course, these examples do not include all the types of models we use at SEKASA for our forecasting tool. That will remain our trade secret 🔒   1. Linear Regression Model (Econometric):   - Utilizes macroeconomic indicators (e.g., GDP growth, inflation, interest rates) as predictors to estimate relationships with stock prices. 🔍   2. Autoregressive Integrated Moving Average (ARIMA) Model (Time Series):   - Captures temporal dependencies and trends in stock prices by considering autoregressive and moving average components. 🔄   3. Random Forest Regression (#MachineLearning):   - Utilizes machine learning ensemble techniques to predict stock prices, incorporating features such as macroeconomic indicators and industry-specific data. 🤖   4. Vector Autoregression (VAR) Model with Lagged Variables:   - Considers interdependencies among stock prices and macroeconomic indicators, recognizing the influence of lagged variables. 📉   5. Arbitrage Pricing Theory (Factor Model):   - Examines the impact of various factors (macroeconomic, industry-specific) on stock returns and valuation, providing a multifactor framework. 💼   6. Engle-Granger Test (Cointegration Analysis):   - Investigates the long-term equilibrium relationship between stock prices and macroeconomic variables, identifying potential co-movements. 🔍 If you are curious about our method of integrating over 1800 different models into our price forecasting tool to create cutting-edge #AI-powered price forecasts, or if you are simply interested in creating different kind of models, please reach out to Katharina Brunkhorst or myself.

🚀Lets dive into the realm of Data strategy and Transformation! In today's data-driven world, data analytics has become a vital skill for businesses and professionals alike. 📶By systematically examining raw data, it can uncover patterns and trends that provide valuable insights into consumer behaviour, market trends, and much more. 💡Join us for a captivating conversation with Dr Anuradha Bhatia, Head of Data Analytics & AI at Standard Chartered Bank to gain valuable insights into how this visionary speaker is leveraging data-driven strategies to propel the organization forward. 🔎 Dr. Anuradha Bhatia offers her insights to some of the most significant questions in the data analytics field, including but not limited to: 1. Exploring emerging technologies, and how to integrate AI/ML capabilities into product development to enhance customer experiences. 2. Addressing security concerns throughout the data lifecycle, from inception to deployment, particularly in projects related to fraud detection and other sensitive domains.  3. How to ensure alignment between data strategy and business strategy, with an example where this alignment led to a significant positive impact? #quanticawards #QuanticDataAnalytics2024 #dataanalytics #datascience #bigdata #bigdataanalytics #analytics  #machinelearning #programming #artificialintelligence #cloudplatform #datascientist #ai #data #api #generativeai #businessanalysts #dataenthusiasts #datadriven  #thoughtleadership #insights #strategy #datamanagement #b2bevents #innovation #linkedinconnections #linkedinnetworking #2024trends #speaker #interview #industryinsights #knowledgesharing

Financial performance enhancement through data-driven Insights. As data becomes a privilege instead of a commodity, C-suit executives and operation teams look for methods to leverage it and make insightful decisions for improving their operational efficiencies, finances, and overall performance. This white paper takes an example of credit card products and one of the key metrics, ‘Cost-to-income Ratio’, that measures operational efficiency. We discuss the levers available to banks and financial institutions like advanced data analytics, machine learning (ML), and decision sciences to gain insights about customer behavior and business operations and take proactive decisions to positively influence their performance metrics. We will also cover the role of predictive analytics for institutions to stay competitive by leveraging near real-time information from their internal data sources and external sources, such as social media, market trends, news feeds, etc. This white paper delves into: 1. Insights of ‘Cost-to-income Ratio 2. Customer Data Platform (CDP) 3. Nagarro's POV through a reference implementation 4. Recommendations for improving the cost-to-income ratio #banking #nagarro #operationalefficiency #predictiveanalytics #ML #customerdataplatform #cdp #costtoincome #financialoptimization https://lnkd.in/dc4JrhaS