How AI and RPA is used in AP Automation to Maximize Business Value?
How AI and RPA is used in AP Automation to Maximize Business Value?
Technologies like AI and RPA in isolation does not mean much to a Finance function or a Business organization at large. One has to have a deep understanding of one’s current processes, re-engineer the current processes on “lean management principles” and then automate the re-engineered process to realize the business value. The business value can be in terms of cost savings, improved quality, improved compliance, improved customer satisfaction.
In spite of lot of hype and investments many AI and RPA project implementations does not yield the desired ROI and even sometimes it fails. I strongly believe that striking the right balance between People, Process and Technology is the key to successful implementation and realize the ROI.
How Artificial Intelligence can be used for Finance Operations?
According to finance leaders responding to the Gartner 2020 Agenda Poll, shows that the top three initiatives of CFOs and Finance leaders are going to be:
- Finance analytics
- Finance organization strategy and structure
- Finance technology optimization
The number of companies adopting AI has increased from 10% four years ago to 37% in 2019, according to Gartner’s survey of 3,000 CIOs. Though the adoption rate is increasing there has been cases reported by CFOs and CIOs that they have not been able to realize the ROI due to implementation issues.
AI can be used abundantly in processes which involve auditing of financial transactions. Also when it comes to analyzing an enormous number of pages of the tax changes, AI can be of great help. It can be expected in the near future to see companies relying on AI to make significant firm related decisions. AI also has the capability to identify how customers are going to react to various situations and problems. Artificial Intelligence is going to help people and firms make smarter decisions at a very quick pace.
Now let us see how AI has transformed the Finance Industry.
1. Risk Assessment: Using AI multiple documents can be scanned, structured and un-structured data can be extracted, and the AI engine can recommend the Loan or Credit level which can be granted to an individual or a borrowing organization.
2. Fraud Detection and Management: AI can use past spending behaviours on different transaction instruments to point out odd behaviour, such as using a card from another country just a few hours after it has been used elsewhere, or an attempt to withdraw a sum of money that is unusual for the account in question. Another excellent feature of fraud detection using AI is that the system has no qualms about learning. If it raises a red flag for a regular transaction and a human being corrects that, the system can learn from the experience and make even more sophisticated decisions about what can be considered fraud and what cannot. This is a case of Machine Learning(ML).
3. Financial Advisory Services: An AI based financial advisory can help you make wise decisions related to your investments. Sometimes an human advisory may have own personal bias while advising, while AI will not have this bias. This can be your personal financial guide giving you advice on anything from investing your money in properties to buying a new car.
4. Trading: Investment companies have been relying on computers and data scientists to determine future patterns in the market. As a domain, trading and investments depend on the ability to predict the future accurately. Machines are great at this because they can crunch a huge amount of data in a short while. Machines can also be taught to observe patterns in past data and predict how these patterns might repeat in the future.
AI can suggest portfolio solutions to meet each person’s demand. So a person with a high-risk appetite can count on AI for decisions on when to buy, hold and sell stock.
5. Managing Finance: An AI based application can be your Spend Analyst and advisory. It accumulates all the data from your web footprint and creates your spending graph. Advocates of privacy breaching on the internet may find it offensive but, maybe be this is what lies in future.
How Robotic Process Automation can be used in Finance Operations?
RPA is an application of technology, governed by business logic and structured inputs, aimed at automating business processes. Using RPA tools, a company can configure software, or a “robot,” to capture and interpret applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems.
Let us consider a relatively simple example of RPA before we explore the full potential of RPA.
Enterprises are still struggling with a lot of manual interventions to undertake reconciliation of book of accounts. By using RPA, your GST reconciliations could be significantly simplified. The bot downloads the GSTR 2 statement, downloads the statement from your ERP, does the required formatting for reconciliation, reconciles the information based on the various rule sets defined and sends the follow up communications to both, the internal teams as well as to suppliers.
This above solution ensures a hassle free and error proof way for any enterprise to match their GST records using RPA.
RPA provides organizations with the ability to reduce staffing costs and human error. David Schatsky, a managing director at Deloitte LP, points to a bank’s experience with implementing RPA, in which the bank redesigned its claims process by deploying 85 bots to run 13 processes, handling 1.5 million requests per year. The bank added capacity equivalent to more than 200 full-time employees at approximately 30 percent of the cost of recruiting more staff, Schatsky says. (Source: CIO.com)
Walmart, Deutsche Bank, AT&T, Vanguard, Ernst & Young, Walgreens, Anthem and American Express Global Business Travel are among the many enterprises adopting RPA.
Walmart CIO Clay Johnson says the retail giant has deployed about 500 bots to automate anything from answering employee questions to retrieving useful information from audit documents. “A lot of those came from people who are tired of the work,” Johnson says.
Enterprises can also supercharge their automation efforts by injecting RPA with cognitive technologies such as Machine Learning(ML), speech recognition, and natural language processing(NLP), automating higher-order tasks that in the past required the perceptual and judgment capabilities of humans.
Such RPA implementations, in which upwards of 15 to 20 steps may be automated, are part of a value chain known as intelligent automation (IA).
By 2020, automation and artificial intelligence will reduce employee requirements in business shared-service centres by 65 percent, according to Gartner.
Spending on RPA tools will top $1 billion in 2019, but will ratchet up to $1.5 billion by 2020, according to Forrester Research, which says that 40 percent of enterprises will operate automation centres and frameworks in place this year.
Even if CIOs navigate the human capital conundrum, RPA implementations fail more often than not. “Several robotics programs have been put on hold, or CIOs have flatly refused to install new bots,” Alex Edlich and Vik Sohoni, senior partners at McKinsey & Company, said in a report.
The platforms on which bots interact often change, and the necessary flexibility isn’t always configured into the bot. Moreover, a new regulation requiring minor changes to an application form could throw off months of work in the back office on a bot that’s nearing completion.
5 Ways how AI and RPA is used in AP Automation
Prevent Late Payments
Avail Early Payment Discounts
Resolve Process Bottlenecks
Improve Supplier Relationships
1. Prevent Late Payments: Intelligence built into AP automation solutions scans historical trends and identifies patterns, then aggregates the data into predictive analytics that predict the probability of on-time payments in the AP process. AP teams will easily see invoices at risk for late payment and be able to drill into the details to determine what actions need to be taken to prevent late payments.
2. Increase e-Invoices: Not all suppliers send the invoices in a format which are readily accepted by the invoice processing organization. Some supplier’s invoices would cause more rejection or exceptions than other suppliers. This can be captured, analyzed by the automated system. Further work can be done with those specific suppliers and encourage them to send e-Invoices which can be easily processed.
3. Avail Early Payment Discounts: A dashboard will show the available early payment discounts available on the invoices at any point of time. The company can take a decision of availing it or not depending upon the availability of Working Capital. If any early payment discounts were missed, those cases can be rectified for upstream issues so that they are not missed in the future.
4. Resolve Process Bottlenecks: Analytics related to processing times enable AP to better understand why certain invoices take longer to process, have more exceptions than others, and tend to get delayed in the workflow. With this data, they will be able to detect and resolve process bottlenecks and streamline back-office functions.
5. Improve Supplier Relationships: A bot can respond to typical supplier queries like, “Is my invoice accepted?”,”Is my invoice approved?”, “Is my payment done today, so that I can check in my account?” etc. Further, automated AP helps in timely and accurate payment to suppliers. This in turn improves supplier relations. Better supplier relations helps AP and Procurement teams negotiate better payment terms with the suppliers.