ARM Insight collects, transforms, and creates actionable insights for 1,000+ financial and retail organizations. With vast amounts of data at their disposal, they bring ideas that serve as input for AI, while keeping privacy and security in place. The company’s unique proposition to the market lies in its ability to safely and securely organize financial data for machine learning, testing, and creating the algorithms necessary for the same. This is done by creating synthetic data from the original transaction data sets. While speaking with CIO Applications, Randy Koch, CEO of ARM Insight, delves into the challenges faced by the finance and retail sectors in processing data for better profits, the successful deployment of technologies by ARM Insight to mitigate these challenges, and the future of the company. A successful technology and services executive with experience in leading startup and multinational companies, Koch has established joint ventures with Siemens, Samsung, and Mitsubishi, during his earlier stints for enabling global technology and service capabilities.
1. Who are your clients? What are the pain points that your clients complain about in their process, and how do you solve them?
The industries that we serve primarily are financial organizations, bank processors, payment companies, and retail firms. We serve businesses that want consumer insights from financial transaction data. One of the pain points that we often come across and address is that our customers want to get a lot closer to their consumer. Right now, they don’t have a full picture of the data that gives them insights into what that the consumer is doing. We answer questions about the consumer’s spending pattern and the increase or decline of card usage. An excellent example on the retail side will be someone going into a sporting goods store. Did they make a purchase within the store or go online and buy a product two or three hours later? On the financial side, we can inform banks, processors, and payments companies about the credit card priorities of the customer and their usage.
The two vital steps are formatting data and—with the technology that we have at ARM Insight— creating what we call synthetic data, a ‘fake’ version of the original dataset that can be applied to AI
To get the most value out of AI, you need massive amounts of data that’s clean and formatted correctly. That’s the single biggest challenge of AI today. There’s a lot of good ideas around AI, but if you don’t have the right data in enough amounts to point it at, you’ll never be able to utilize the real value out of it. ARM Insight safely and securely aggregates that massive financial dataset back to the industry for developing productive AI solutions and getting closer to consumer spending patterns and trends.
3. Is there a step-by-step process involved in delivering the solutions to the clients?
ARM Insight has done this for more than a thousand banking institutions and retailers. We’ve helped them create a data monetization strategy in their roadmap to AI. The two vital steps are formatting data and—with the technology that we have at ARM Insight—creating what we call synthetic data, or a “fake” version of the original dataset, that can be applied to AI, while protecting data privacy. From there, you can ask the right AI questions and then monetize the data.
4. Is there a particular success story or a case study where you have helped a client with what they wanted?
One of the biggest challenges that we keep hearing from our banking clients is how to get to the top of the clients’ wallets with their credit or debit card. A key indicator of loyalty and customer lifecycle is how often they use one card versus the others. We took the given financial institution’s data and cleaned it up, then applied machine learning and AI on top of it and found some exciting trends. If consumers spend at specific merchants say Facebook, Lyft, Uber, and PayPal, they are going to be top of the wallet, and will be conducting forty to fifty transactions per month. On the contrary, if they’re not spending a lot with these companies, it may be a sign of them leaving for another payment card. That’s an excellent use for banks where AI was not only used to show them how to retain and get more money out of customers, but also raise a flag to a customer’s potential departure.
On the retail side, the first step is knowing where the consumers shopped and transacted an hour or two before. We then break the information down into generation-based categories. Knowing where the millennials and baby boomers go respectively, we can provide that data and intelligence back to the retailer. Another everyday use case we see in the retail world is competitive analysis. If you’re the brick-and-mortar retail store owner for sporting goods, does a particular person who’s coming into your store buy or do they shop online? Such insights and how to retain those consumers are some of the value we provide back to our customers.
5. What do you envision as the next big step for the company? Is there a plan to extend the number of solutions and the features that you deliver right now?
Our mission is to become the de-facto source for unique and invaluable insights for financial data. We will continue to provide more diverse financial data and synthesize it to give that back to the industry and develop AI solutions on top of it. Our current aim is to get more diverse data sets, optimize the data more precisely, and stay in the present course, attracting finance and retail companies for forging valuable partnerships.