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In this session, learn about how to get started with AI to create business impact. Join Anupam Kunwar, AVP, Data and Analytics, HighRadius as he shares insights on the opportunities and challenges of Artificial Intelligence and how it is affecting the treasury and finance functions.

On Demand Webinar

AI & the Finance Professional:

Managing the Evolving Business

Session Summary

In this session, learn about how to get started with AI to create business impact. Join Anupam Kunwar, AVP, Data and Analytics, HighRadius as he shares insights on the opportunities and challenges of Artificial Intelligence and how it is affecting the treasury and finance functions.

Key Takeaways

AI is deeply rooted in consumers' lives in the modern world
  • Artificial intelligence (AI) is progressively becoming a part of the customer experience
  • AI is now utilized everywhere, from product and entertainment suggestions to voice enabled digital assistants
Adoption of AI in the business and consumer worlds
  • One of the reasons for AI’s adoption in the consumer space is its ability to handle massive amounts of data while maintaining consistency
  • Businesses have adopted AI because of the availability of tools, cost-benefits, and smaller data size
  • Artificial intelligence is widely utilized in fields such as fraud detection, credit scoring, and regulatory compliance
Step-by-step guide for AI implementation
  • Before implementing, organizations need to ensure that they have exact data to train the models and test the results
  • Comprehending the working of AI is key, since the predictions, insights, and recommendations are sensible for the business or not
  • Quality and availability of data can have an impact and limit AI’s potential
Getting started with AI for your business
  • How to make meaningful decisions while getting started with AI
  • Important questions to ask for starting the AI implementation process with specific output in mind

Anupam Kunwar [0:01]  

Hello and welcome everyone. My name is Anupam Kunwar, and I lead the Treasury data science team at HighRadius. HighRadius is an enterprise SAS company that uses artificial intelligence for Treasury and finance applications. Today we will talk about the evolution of artificial intelligence or AI in short, and how it impacts treasury and finance professionals everywhere.

Anupam Kunwar [0:29]  

We will discuss three things today. How is AI affecting us as individuals and as professionals, things that Treasury and finance professionals need to understand in this evolving world of AI? And things to keep in mind as your business or your department starts thinking about AI.

Anupam Kunwar [0:46]

AI is here and it’s going to stay, there’s no doubt about that. This is the reality.

Anupam Kunwar [0:52]

As consumers in the modern world, AI is already deeply rooted in our lives. We can’t think of life before we started interacting with AI. For the last 15-20 years, AI in a variety of forms has increasingly become a part of the consumer experience. Today from product and entertainment recommendations, driving directions on-call ride-sharing cabs, voice-enabled digital assistants, AI is used everywhere.

Anupam Kunwar [1:22]  

Consider this modern home. By the way, this is not my home. The thermostat learns from user behavior and adjusts the temperature based on user preference. The Smart TV understands viewer preference and can make recommendations for what the user wants to watch next, music is played as per your mood and preferences. Even the lights are just based on the environment.

Anupam Kunwar [1:45]  

We don’t even notice it anymore. We can speak out loud and have our wishes fulfilled. As consumers AI has clearly arrived and is here to stay. There’s no doubt about that.

Anupam Kunwar [1:57]  

But what’s happening in the work environment.

Anupam Kunwar [2:01]  

Why did AI adoption start in the consumer space 20 plus years ago, there were three reasons. One, the problem was worth solving. Just think about how large the consumer internet players’ revenues and profitability are.

Anupam Kunwar [2:16]  

Number two, the amount of data was large and consistent. And that’s a very important point. Number three.

Anupam Kunwar [2:24]

Then specialized skills were worth acquiring a lot of data science PhDs were hired in these companies

Anupam Kunwar [2:32]

cutting to today, business adoption has started.

Anupam Kunwar [2:37]

That’s again driven by three things. One, the tools and skills have become more commonly available now to these cost-benefit trade-offs now are starting to make sense, and three. Even with the typically smaller data size of company-specific data, results can now be obtained. This is why we are starting to see an uptick in the business adoption of AI. Many leading large and small companies are already using AI in the treasury and finance departments. AI is extensively used in areas such as fraud detection, credit, scoring, and certain types of regulatory compliance. These are all fields where the data

Anupam Kunwar [3:16]

and rules are very well defined and are very consistent across various businesses. This makes it easier to implement AI.

Anupam Kunwar [3:24]  

On paper, almost any process with lots of data could be made more accurate with AI. For example, one could even predict how many times the main door in a building opens. But is this a problem worth solving? Unless you’re in the cold storage business? Probably not.

Anupam Kunwar [3:43]  

This business decision is ultimately yours. Other potential insights and decision improvements worth the investment? How high is the bar? That’s for you to decide.

Anupam Kunwar [3:55]  

So what does this mean for us as finance and Treasury professionals? What do we need to understand about how this stuff works? So we can make intelligent decisions about how to drive its usage most effectively in our business.

Anupam Kunwar [4:10]  

When implementing AI there are three things to understand. One, the first step, make sure that we have the data that can be used to make decisions. This data is used for training the models and testing the results. Without data, there is no way it’s very important.

Anupam Kunwar [4:29]  

The data is used in a variety of different ways to get results. In this process DATA step. There’s a lot of complexity and technical jargon that gets thrown about, but don’t let it fool you. The choice and trade-offs between complex algorithms, complex techniques, and tools are very dependent on the data. The type of results required. And it’s really driven by an analysis of the data in the past results. But the specifics of how it works are less important than whether it makes sense for your data.

Anupam Kunwar [5:00]  

Data and gives you results that are useful for you. At the end of it all that matters is does it deliver meaningful results for the chosen problem? That’s always the bottom line? How well does it work for the problem at hand?

Anupam Kunwar [5:14]  

And that’s the third part of this of how AI works, the predictions, the insights, the recommendations have to make sense for your business. Do we get sensible and consistent outputs? Do they pass the smell test? Is there a way to estimate how well they would have performed in the past, always start with the end in mind is the problem worth solving.

Anupam Kunwar [5:37]  

AI has certain limitations we need to be mindful of as a caveat. Most AI applications do not start working from scratch, they need to be trained on significant data, they require some time to learn and grow and be trained before they can be useful. So it’s not going to be the flip of a switch, they’re highly dependent on the quality and availability of the training data, there is not much data, it will be very difficult to get accurate results.

Anupam Kunwar [6:04]  

And finally, your business’s unique, even programs that have been trained on businesses similar to yours will have to take into account variations such as how you label your data, the frequency at which you report the data, the different systems and connections that you have, the different types of processes you have.

Anupam Kunwar [6:24]  

So while it’s valuable to have experience, it’s also important to have processes to account for the differences between businesses, when you’re implementing these kinds of tools.

Anupam Kunwar [6:36]  

Given all of these opportunities and limitations, it’s important to really be thoughtful about how AI might be applicable to your business. It’s really important that we start the process of an AI implementation with a specific output or end in mind, such as when we lay out balances turn into cash. How should I plan an intercompany? Transfers next month? How should I plan for deleveraging my balance sheet?

Anupam Kunwar [7:02]  

What will the cash balance in Region X? Be on a date? Why?

Anupam Kunwar [7:08]  

These are all examples of meaningful decisions that can be made better with the use of AI.

Anupam Kunwar [7:14]  

So when you’re considering AI in your business, there are four key questions to think about. Number one, the problem you’re trying to solve? Does doing it better bring long-term benefits? Is the output something useful to our business? Can the output help make better decisions? Second, do we have enough data to train this on? Would it help if other people had solved this problem for other businesses? Three? Is this product going to evolve and learn as business circumstances change? Or is it going to be inhibited? Does it have some static rules that won’t be flexible in different business circumstances? And fourth, who will maintain, update, retrain and validate that models are doing what they’re supposed to be doing? From the data consumption to the point where the output goes into a business process? Will it be the finance team that maintains this? Will it be the IT team? Will it be a partner? Will it be a vendor? How will the governance and maintenance surrounding this be set up?

Anupam Kunwar [8:14]  

These are all things that need to be understood from the outset before you dive into this.

Anupam Kunwar [8:21]

Thank you for your time. If you have questions or want to know more about ideas, please visit our virtual booth. Thank you

Anupam Kunwar

AVP, Data and Analytics

Almost any process with lots of data could be made more accurate with AI. But, it is for the businesses to decide if the potential insights and decision improvements are worth the investment.


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HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.