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RPA in Treasury Management in '24: 5 Ways It Helps Liquidity

According to the latest reports 1, liquidity risk – the failure to have enough cash on hand to cover short-term costs – in the EU is now as high as contagion and operational risks. 

The responsibility of ensuring that a company’s liquidity risks are minimized lies with its treasury department.

Limited visibility into accounts receivable, for instance, could lead to poor cash flow estimates and cause liquidity risks and even bankruptcy. MF Global, a company with $41B 2 of assets under management, went bankrupt in 2011 because of liquidity shortages.3

RPA in treasury management, alongside other automation tools, such as intelligent automation, intelligent document processing software (IDPS) – or leveraging TMSs (treasury management systems) for end-to-end automation – can improve the treasury management process. 

In this article, we will discuss the challenges of manual treasury management, explain what treasury management automation is, and discuss the five use cases of RPA in treasury management.

What is treasury management?

Treasury management is a subcategory of finance management. Its premise is to manage the liquidity, and by doing so, to maintain the short-term financial stability of a company. 

For instance, in the O2C process, the accounting department issues the customer’s invoice, and the treasury department ensures that the invoice receivables are received on time. 

Other facades of treasury management include: 

What are the challenges of manual treasury management?

1. Sales channels monitoring 

86% of companies in 2020 claimed to have increased their brand awareness through digital channels. As companies do that, they can sell in markets which they haven’t done before. 

The flipside of this is the need for constant monitoring. B2B interactions today take place via 10 different channels (see Figure 1). It’s a challenge to manually monitor where your sales are coming from to: 

  • Record them, 
  • Analyze them, 
  • Extract their data, 
  • Collect the payments, etc.    
A timeline, spanning from 2016 to 2021, showing the number of different sales channel that a typical B2B customer uses. It's currently at 10.
Figure 1: A typical B2B customer in the US today uses around 10 different sales channels. Source: McKinsey

2. New risk exposures

A company’s growth might open new investment opportunities, albeit with unique heterogeneous risk factors straining the company’s liquidity positions. 

A typical risk is of foreign exchange (FX): The possibility that businesses investing abroad might receive income in the foreign country’s depreciating currency and economy.

Expanding investment portfolios by investing in “novel” projects, such as digital currencies, the metaverse, self-driving cars, etc., is another example of new risk exposures. 

All such risks can affect the asset’s performance, disrupt its return on capital, and threaten the liquidity of the company investing in it. It’s important to monitor the real-time data of each asset’s performance, and the market that the asset belongs to, for a comprehensive risk assessment. 

3. Compliance issues 

Companies may be restricted, via their shareholders’ agreements 4, from, for instance, investing more than a certain percentage of their equity into instruments that fall below a certain risk tolerance. 

Or they might governmentally be prohibited from investing in projects with negative environmental impacts.5 

It’s a part of treasury management to ensure that investments are in-line with the company’s: 

  • Strategic goals, 
  • Shareholders’ agreements, 
  • Environmental responsibilities, etc. 

4. Lack of visibility 

Because it takes a lot of time and effort to compile them for archives, move them from one location to another, and search through them for information, paper-based processes limit visibility into the business operations. 

For instance, if T&E (travel and expense) management is done manually, underestimation of costs, or inaccurate calculation of entries, could give the false image of costs being lower than they actually are. 

In a similar fashion, a company might have limited visibility into its assets-to-debt ratio. In 2020, for instance, J. Crew, the American apparel giant, filed for bankruptcy after being overleveraged.6 

Especially with multinational companies, or large conglomerates, it can be challenging to monitor each and every transaction manually. 

What is RPA in treasury management?

To answer how RPA can help treasury management, we should first know that RPA is a part of the larger treasury management automation ecosystem.

What is treasury automation?

Treasury automation is leveraging business process automation software to automate financial operations for working around the aforementioned 4 challenges. 

In 2022, more than 66% of treasurers worldwide claimed they had “accelerated” their adoption of automation solutions, compared to 55% last year (see Figure 2). 7 

A pie graph showing the varying tendencies of treasurers vis-a-vis automation initiatives.
Figure 2: 66% of treasurers claimed in 2021 to have accelerated their automation initiatives. Source: Flow

RPA is an ideal candidate for finance automation – and treasury management – because most of the finance processes are rules-based. This could make RPA one of the technologies powering BPM software. 

What are the use cases of RPA in treasury management?

1. Automated reconciliation 

RPA can automate the accounts reconciliation of receivables. Companies can program RPA bots to monitor a company’s account for the arrival of receivables from a specific client. 

For instance, assume a random payment arrives. The intelligent RPA bot could use its (embedded) OCR capabilities to read the invoice, extract critical information (sender’s name, amount, description, etc.), and compare the data against the rules-based input and auto-match it. 

If the payment meets the sales order’s criteria, the bot can automatically reconcile the payment by making an entry into the sales books and settling the transaction 

RPA in treasury management eliminates the need for human staff to dedicate their time to monitoring receivables and doing the exact thing a bot would do. The extrication is particularly important for businesses that usually receive a large volume of low-denominated receivables which are harder to keep track of than a small volume of large receivables. 

2. Forecasting risk factors

Treasurers can forecast the risk factors via a combination of RPA, API, and AI/ML models. 

For instance, Deutsche Bank is creating a solution that (see Figure 3): 

  1. Extracts customer’s data from different ERP solutions via API and RPA,
  2. Structures and normalizes them, 
  3. Complements customer’s data with other market data (such as uncollected debts, FX exposure levels, assets in circulation, etc.
  4. And forecasts the company’s working capital, ESG KPIs, FX positions, and other factors with respect to #3. 
A graph showing Duetsche bank using RPA in treasury management, alongside different automation solutions, to automate its treasury management.
Figure 3: Duetsche Bank is using RPA in treasury management initiatives, alongside other automation tools, to create an end-to-end automation solution. Source: Flow

Although this use case is not the exclusive use case of RPA in treasury management, the software still plays an integral part in building the framework for end-to-end treasury management automation.

3. Debt collection 

RPA bots can assist in the debt collection process to make sure that the cash flow is not adversely affected. RPA bots can help in: 

  • Extending payment deadlines within a particular time frame, subject to predetermined rules and conditions. 
  • Sending payment reminders to the debtor if their payment has not been reconciled by the maturity date. 
  • Having RPA-assisted chatbots can free reps from answering common customer queries regarding payment and allow them to focus on more strategic tasks. 

So RPA in treasury management also entails ensuring that debts are collected on-time so there is always enough cash on hand in the short-term to cover costs.

4. Automating investments

As mentioned, maintaining an optimum investment strategy ensures healthy cash inflows and enables the company to, at the very least, meet its short-term debt obligations. 

For low-risk investments – such as those in index funds (see Figure 4) or treasury bills – treasurers can schedule the RPA bots to purchase additional shares of the funds periodically. 

A screenshot showing the volatility of NASDAQ's index fund. RPA in treasury management can automate investments in low-risk projects such as this.
Figure 4: RPA can automate low-risk, pre-approved investment routes, such as a low-volatile NASDAQ index fund. Source: Fred

Therefore, RPA in treasury management enables some investment decisions to be taken on autopilot and without human interference.

5. Up-to-date balance sheet

One method of eliminating liquidity risk is maintaining an up-to-date balance sheet that accurately reflects a company’s financial health. 

Not paying off debts on time accrues interest, bites more into a company’s revenue, and can create an incomplete balance sheet. RPA bots can be programmed to make automatic payments to suppliers once invoices are due. 

A telecom company, for instance, spent ≈14 days processing a single payment, resulting in 2-3% late fees every time. 8 And if the bill happened to be an energy or water bill, late payments also resulted in service disruptions, which halted operations and hurt the company more. 

Leveraging RPA in treasury management efforts allowed them to automate invoice data extraction, processing, and payment. 

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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