Heard of Amy, Ceba, Erica, Eva, or Kai?
If you haven’t, then you’ve missed out on some stellar AI action in finance. Amy, Ceba, Erica, Eva, and Kai are AI-driven chatbots used by HSBC, Commonwealth Bank of Australia, Bank of America, HDFC, and fintech provider Kasisto, respectively.
Amy, HSBC’s chatbot (Source)
Banking and finance teams work on large data sets and have mundane workflows involving data entry, calculations, and matching data records. Such workflows are well-suited for automation and the application of artificial intelligence (AI) to save time and effort.
Banking and finance teams have often been the first ones to try AI-based tools, helped by the large volumes of data they hold. This large volume of data makes the training of AI and machine learning (ML) models easier.
In this article, we look at some proven AI use cases in finance that mid-market companies can consider.
Reconciliation is a crucial step for any finance department. The reconciliation process ensures that two sets of records match. For example, your ERP data and cash flow statement must match your bank account entries. Your invoice amounts, customers payments, and entries in sub-ledgers should also tally up.
Manual reconciliation methods are tedious, time-consuming, and error-prone. Yet, 58% of US companies rely on manual reconciliation methods. A PwC report estimates that 30% of a finance team’s time is spent on manual reconciliation. One out of four (25%) businesses experience delays during the closing of their books due to account reconciliation problems.
AI for reconciliation
AI-powered reconciliation tools and algorithms help save time and reduce errors by automating the steps involved. Reconciliation involves looking for duplications, date or time discrepancies, and matching records stored in multiple files or systems. As a mid-market business, any delay in the reconciliation process can have a cascading effect on your funds. Because of the nature of repetitive tasks involved in matching data, it becomes a classic use case for deploying machine learning and AI to simplify the process.
ML and AI help move beyond Boolean rules to understand your data and build models that have a higher degree of confidence in matching. Embedded machine learning or AI-based systems use the data trails left by you to learn more accurately what is deemed a match.
While banks, corporates, and tech firms are working on creating fully AI-powered reconciliation processes, many firms have implemented robotic process automation (RPA) based tools to automate reconciliation. Unlike AI-driven systems, RPA tools require some element of human intervention or decision-making to complete the processes.
A US-based wealth management firm was using manual methods to reconcile data from multiple sources such as Excel, PDF, emails, and over 200 websites used by the firm’s financial advisors. The process lacked control, supervisory approval, and review workflows.
To improve efficiency, the firm implemented a reconciliation platform with machine learning capabilities. The platform’s reconciliation engine was trained on a large volume of data and used probability-based algorithms to identify exceptions and mismatches. Once the system was trained it was able to automatically check data quality within specified tolerance ranges, generate exception rules, and provide ready reports to the accounting team. This helped eliminate operations risks involved in manually copying and pasting data and checking records. The firm was able to save several million dollars per year due to improved efficiencies. (Source)
AI-driven models to predict credit risk and block orders in Credit Cloud
|Case study A UK High Street bank was experiencing bad debt challenges and wanted to try machine learning to predict customers’ probability of default rather than rely only on credit scores. It used the services of a third-party tech vendor to build thousands of ML models based on the data that it had accumulated (220 million rows of data!). The AI platform can also clean and transform the data fed into its models. With this AI-based model, the bank could catch 83% of probable defaulters not identified by credit scores. (Source)|
The customer is central to your business. Delighting your clients with quick responses to their queries and smooth access to services is key to building great customer relationships.
But managing all customer requests manually is expensive and time-consuming. It can also lead to customer dissatisfaction due to long wait times and rude support staff behavior.
AI for customer service
Today, finance teams use a variety of AI-based tools to improve customer service. One of the AI-based tools that banks and companies use is chatbots to help customers get quick resolution of their queries. Chatbots built on AI and natural language processing (NLP) platforms are more intelligent and can answer varied types of questions compared to rule-based chatbots. This helps your finance team save the time they otherwise spend to answer mundane, repetitive queries.
Erica, the virtual assistant offered by Bank of America (Source)
AI and ML tools also help onboard customers faster by automating repetitive tasks such as filling forms and verifying client details. Businesses are also using AI-based models to predict customer churn and delinquency. Forecasting these metrics requires you to comb through multiple large datasets. AI models automate this process as well as offer higher accuracy compared to manual methods.
Allstate Business Insurance wanted to provide faster responses to small businesses that had questions around insurance, deductions, claim processing, and related laws and regulations. They implemented an AI-powered chatbot, ABIE, to provide real-time answers to questions small business owners had. Since the chatbot is AI-powered, it adds to its database questions asked by clients and evolves its responses to meet changing requirements. (Source)
It can be tempting to try your hand to automate myriad tasks that your team performs daily. Beware, you could end up burning your hands and pockets! Careful assessment of workflows and functions that can be automated is essential to get a good return on investment (ROI).
Estimate the costs involved in implementing AI as well as the returns you expect, and choose functions that are likely to deliver a higher ROI. If you’re new to AI technologies, consider investing in proven use cases rather than in untested solutions. Research what your peers are doing and keep abreast with the developments in the AI space.
Identify and shortlist tech vendor partners you can work with to ensure successful implementation and support for your AI projects.
At HighRadius, we offer AI-powered order-to-cash automation software for mid-market companies and enterprise organizations. Our products include modules for e-invoicing, collections, credit risk management, cash application, and deductions. We also have software to automate treasury operations and record-to-report functions.
Automate invoicing, collections, deduction, and credit risk management with our AI-powered AR suite and experience enhanced cash flow and lower DSO & bad debtTalk to our experts