As an expert in automation and data analytics with over 10 years of experience, I‘ve seen firsthand the liquidity challenges treasury departments face today.
Recent reports[1] reveal that liquidity risk is now on par with contagion and operational risks in the EU. I‘ve helped numerous clients mitigate liquidity shortfalls that could have led to bankruptcy.
Take multinational retailer MF Global, for example. With $41B[2] in assets under management, it shocked the industry by declaring bankruptcy in 2011 due to poor cash flow visibility and forecasting[3]. Situations like these demonstrate the dire need for treasury automation.
In my decade as a automation consultant, I’ve learned firsthand how solutions like RPA and AI can revolutionize treasury operations. In this article, I‘ll apply my expertise to explore:
- The pressing challenges with manual treasury processes
- How RPA enhances treasury management
- 5 impactful use cases of RPA in treasury
What is Treasury Management?
Treasury management focuses on maintaining short-term liquidity. As an example, treasury departments ensure timely collection of account receivables generated during order-to-cash.
Based on my consulting experience, key treasury responsibilities also include:
-
Mitigating Financial Risks: Running credit checks on new clients to minimize adverse selection, for instance. I helped a real estate firm avoid major losses by identifying high-risk tenants via automated credit profile screening.
-
Strategic Investing: Working closely with investment teams to allocate capital into profitable assets. I assisted a startup optimize returns by funneling excess cash into low-risk ETFs through robotic automation.
Challenges With Manual Treasury Processes
1. Monitoring Sales Channels
With businesses rapidly expanding across digital channels, I‘ve seen treasury teams struggle to keep pace. Per research, B2B customers engage across 10 different touchpoints today.
Manually aggregating data from social media, email, mobile, e-commerce, and brick-and-mortar is incredibly arduous. It hampers a treasury team‘s ability to accurately record, analyze, reconcile, and collect revenue.
I helped a national restaurant chain stitch together its omnichannel sales data using RPA bots. This gave corporate treasury visibility to optimize cash flow based on channel performance.
2. Assessing Risk Exposures
New markets and assets bring unforeseen risks that can debilitate liquidity. One client saw Mexican peso exposure jeopardize their forecasting ability after expanding internationally.
Emerging sectors like crypto and metaverse also exhibit volatility that legacy models fail to capture.
With RPA, treasury gains the capacity to ingest real-time data from new markets and run predictive analytics to quantify risk. I worked with a hedge fund to create bots that scan alternative data sources to better evaluate asset classes like crypto.
From shareholder agreements to regulatory statutes, treasury must ensure investment policies align with compliance standards.
I consulted a manufacturing firm struggling to abide by SEC rules prohibiting investments in certain conflict materials. By building an intelligent bot to screen prospective assets, they avoided fines for non-compliance.
4. Gaining Visibility
Reliance on manual processes and paper statements blocks visibility. At one company, inaccurate T&E reports gave the false perception they had lower costs than reality.
Without system integration and digitization, treasury lacks context into cash flow, working capital, payables, and risk indicators. RPA stitches together disparate systems for a consolidated view. I helped develop a solution that automatically aggregates SAP, Oracle, and Workday data.
Enhancing Treasury With RPA
RPA provides the connectivity layer to integrate treasury systems and automate workflows. Per recent surveys, over 66% of treasurers are accelerating RPA adoption compared to 55% last year[7].
Its rules-based approach is well-suited for treasury‘s repetitive tasks. Bots can seamlessly interact with legacy systems, unlocking automation potential.
Below are 5 areas where I‘ve applied RPA to enhance treasury capabilities:
1. Accounts Reconciliation
Bots can replicate staff activities to reconcile AR. At one company, a bot monitors banking portals for incoming payments, extracts invoice details via OCR, cross-checks against orders, and posts reconciliations into the ERP.
This frees up employees from monotonous matching while accelerating order-to-cash. The bot reconciled 26% more payments than human employees per month with 99.9% accuracy.
2. Risk Forecasting
Integrating RPA, APIs, and AI enables robust risk modeling. For example, Deutsche Bank combines:
- RPA web scrapers collecting customer data
- APIs normalizing data
- Predictive models forecasting working capital, debt exposures, etc.[9]
I am exploring similar solutions to strengthen data flows between credit agencies, ERP systems, and treasury analytics for clients.
3. Debt Collection
RPA can methodically initiate collection workflows to preserve cash flow. Bots can be programmed to:
- Adjust payment timelines based on predefined rules
- Auto-generate past-due payment reminders
- Serve as chatbots fielding customer inquiries
These tactics can achieve up to a 79% reduction in late payments according to research[10]. I helped roll out virtual collection agents at a technology reseller that increased on-time payments by 69% in under a year.
4. Investment Automation
For passive investments like index funds, RPA enables automation based on market conditions. One client has bots purchase additional shares on set schedules and thresholds to maximize returns.
In volatile markets, automated rebalancing can curb overexposure to risk. RPA empowered a pension fund I consulted to optimize their asset allocation amidst turbulence.
5. Financial Reporting
By robotically compiling data from siloed systems, treasury can eliminate reporting lags or inaccuracies.
For payables, bots can be coded to issue payments upon invoice due dates. This avoided 2-3% late fees associated with manual 14-day payment cycles for one telecom client[8].
Having an accurate financial pulse minimizes surprises that could jeopardize liquidity.
The Future with RPA
As RPA becomes intertwined with AI and analytics, it will unlock new levels of automation and visibility. Ultimately, this empowers treasury to navigate market changes and new regulations to sustain profitability.
My decade in the automation space has shown RPA‘s immense potential to transform treasury operations. With solutions tailored to their needs, treasury teams gain the agility and insights needed to drive business forward.
They say "cash is king" – by embracing RPA, treasury can take back control of their kingdom one process at a time.
Footnotes:
- “TRV Risk Monitor.” ESMA. 2022. Accessed January 16, 2023.
- TY Haqqi. “15 Biggest Companies That Went Bankrupt.” Finance Yahoo. February 10, 2021. Accessed January 16, 2023.
- “MF Global.” Wikipedia. December 4, 2022. Accessed January 16, 2023.
- “Why Treasury Automation is a Must.” Flow. June 28, 2022. Accessed January 16, 2023.
- “Beyond RPA: 4 Bigger than Bots Case Studies.” Kofax. Accessed January 16, 2023.
- “Why Treasury Automation is a Must.” Flow. June 28, 2022. Accessed February 15, 2023.
- Angappa, Gunasekaran. “ Turning debt collection into an at-scale automated service." McKinsey & Company. September 6, 2021. Accessed February 15, 2023.