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)
Whether you’re a bank or a fast-growing business servicing hundreds of clients, managing your credit risk is crucial to avoid bad debt and cash flow problems. A large number of small and mid-sized companies fold up because they fail to manage customer risk and subsequently their cash flows.
Many firms do not use data-driven approaches to manage credit risk. They rely on generic information or on the sales executive’s judgment to decide credit limits. Biased credit decisions eventually lead to your company losing money.
AI for credit scoring
Most organizations have realized relying on salespersons’ judgment or the finance team’s opinion for credit decisions isn’t very effective. They have started utilizing the vast amount of data they have about customers’ payment patterns to create AI-powered models that can answer accurately what’s the chance of a customer defaulting.
Tech companies have also developed sophisticated models using data from multiple sources such as clients’ payment pattern trends, applicant’s revenue or income, expenses, and qualifications. Such AI models provide an accurate picture of a customer’s creditworthiness. The AI-driven credit models also provide credit scores to help rank customers based on their financial health.
AI-driven models to predict credit risk and block orders in Credit Cloud
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)
There are several other applications of AI in finance. We list out some other functions in which AI helps finance teams.
The rise of digital channels for managing financial transactions has also seen an increase in fraud and cyberattacks. AI-based tools analyze data in real-time to identify and predict instances of fraud such as money laundering and imposters, and security threats such as malware attacks.
Banks, financial institutions, and even enterprises invest in a variety of financial platforms including stock markets and bonds to strengthen their assets. AI-based tools help crawl through data from multiple sources and build models that factor in hundreds of parameters. This helps you make better investment and portfolio management decisions.
AI tools are also used to automate daily tasks such as approval workflows, report generation, and internal communications. AI tools can build daily reports tracking key metrics such as accounts receivables, accounts payables, profit targets, and share price movements, and email them to the relevant stakeholders automatically.
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.
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.