RPA in Treasury Management in ’23: 5 Ways It Helps Liquidity
Treasurers today face a daunting task – managing liquidity risk has become extremely challenging. According to a 2022 survey by the European Securities and Markets Authority (ESMA), 66% of European treasurers now rate liquidity risk as high as contagion and operational risks [1].
The pressure is on for corporate treasury departments to ensure adequate cash flow to cover short-term costs and minimize liquidity crunches that could jeopardize the business. But doing this through manual processes can be nearly impossible with the complexity of global business.
This is where robotic process automation (RPA) comes in. By digitally automating repetitive manual processes, RPA can provide tremendous efficiency gains in treasury management activities. Leading enterprises like Deutsche Bank are already using RPA as part of larger automation programs to transform treasury operations [2].
In this article, we‘ll examine 5 key ways RPA can help treasury departments enhance liquidity positions and control risk:
- Automated reconciliation of receivables
- Forecasting risk factors
- Streamlining debt collection
- Automating low-risk investments
- Maintaining accurate balance sheets
But first, let‘s look at why treasury management needs automation in the first place.
The Challenges Facing Manual Treasury Management
Treasury departments rely on manual processes face a multitude of challenges:
Monitoring Omni-Channel Sales and Receivables
Today‘s businesses sell through a multitude of channels – B2B customers use 10 on average [3]. Manually tracking sales and receivables across all these touchpoints is extremely labor intensive and prone to errors.
Without systematizing this data collection, treasurers lack visibility into cash flow. This heightens liquidity risk.
Assessing New Risk Exposures
Entering new geographical markets or asset classes brings unfamiliar risks to measure and monitor. Doing this manually makes it difficult to see how these risks related to overall liquidity.
Limited risk visibility creates uncertainty in cash flow forecasts critical for liquidity management.
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Figure 1: 66% of European treasurers rate liquidity risk as high as operational and contagion risk
Ensuring Compliance
Organizations must comply with regulations limiting certain types of investments. Keeping track of this manually opens compliance gaps that could lead to penalties.
Non-compliance also inhibits implementing an optimal investment strategy, negatively impacting cash flow.
Gaining Visibility into Finances
Limited financial visibility is another consequence of disconnected, paper-based systems. Without a unified view, treasurers can‘t accurately assess liquidity positions.
In fact, 42% of treasurers say lack of visibility into transactions and balances is their top concern [4]. This uncertainty around liquidity makes managing risk precarious.
How RPA Helps Treasury Management
RPA provides a digital workforce to automate the highly manual, repetitive processes common in treasury departments. Sophisticated RPA tools have capabilities like:
- Integrating siloed systems – Extracting and compiling data from disjointed enterprise systems into unified formats
- Process mining – Discovering processes to automate by tracing system logs and user actions
- OCR & NLP – Reading documents and interpreting unstructured data using AI technologies
- Secure web automation – Logging into web systems and portals to perform tasks
These capabilities make RPA an ideal automation method for treasury management activities like:
1. Automated Reconciliation of Receivables
Reconciling client payments against pending invoices is an everyday, yet highly manual activity. RPA robots can replicate a human‘s actions at scale to do this automatically:
- Log into bank portals and retrieve pending payments
- Use OCR to scan remittance data and match to invoice details
- Transfer payment information into the accounting system
- Update invoice status as paid
This automation provides huge time savings by eliminating manual payment tracking and reconciliation. For high volume businesses, RPA dramatically accelerates cash application to improve liquidity.
At Illumina, a biotechnology company, RPA reduced payment reconciliation effort by 80%, enabling staff to focus on value-added activities [5].
2. Forecasting Risk Factors
Since RPA can integrate data across banking, ERP, and other systems, it provides a comprehensive view of risk factors like:
- FX rate volatility
- Portfolio performance
- Market movements
- Operational KPIs
RPA aggregates this data into analytics models to dynamically simulate scenarios and forecast risks. This gives treasurers continuous visibility to make data-driven decisions that ensure liquidity.
At Deutsche Bank, RPA combined with analytics tracks working capital cycles, interest rate risk, and other factors daily to mitigate liquidity risk [2].
3. Streamlining Debt Collection
Collecting from late payers requires considerable manual work. RPA empowers treasury to automate parts of this process:
- Payment reminders – Bots can send automatic email/SMS reminders to debtors on a set schedule
- Deadline extensions – Within prescribed limits, bots can push due dates and resend invoices as needed
- Customer service – RPA can handle common late payment queries to resolve issues faster
This improves collections efficiency and cash flow. Esker, an automation vendor, notes a global tech manufacturerdecreased DSO by 3 days by using RPA for order and payment monitoring [6].
4. Automating Low-Risk Investments
RPA allows treasury to efficiently invest excess liquidity in permitted instruments like money markets and treasury securities.
Bots can purchase pre-approved investments at optimal times based on market conditions and cash levels. This hands-off approach generates returns while avoiding significant risk exposure.
BlackRock found RPA reduced the effort of executing equity and fixed income trades by up to 99%, while improving compliance [7].
5. Maintaining Accurate Balance Sheets
Late supplier payments create liquidity inefficiencies through penalties and inflated payables balances.
With RPA, treasurers can pay invoices precisely on-time by setting bots to process payments on due dates. This eliminates late fees and keeps balance sheets precise.
According to Kofax, a telecom achieved 98% straight-through processing of supplier payments using RPA, compared to just 60% manually [8].
RPA‘s Role in End-to-End Treasury Automation
While RPA offers major benefits alone, it works best as part of an integrated automation solution encompassing:
- Artificial intelligence – Advanced self-learning algorithms for complex tasks
- Big data – Uncovering insights from large, disparate datasets
- OCR/NLP – Text and speech recognition technology
- APIs – Connecting systems easily to move data
This enables end-to-end automation across the treasury department, as seen in Deutsche Bank‘s Cash Management Analytics program [2].
The results are compelling – DBS Treasury Prism, an integrated automation solution, improves treasury efficiency by up to 70% [9].
As part of this automation ecosystem, RPA provides the digital workforce executing routine processes at scale. This human-bot collaboration amplifies the impact of AI/ML innovations.
The Bottom Line
Corporate treasurers today are desperate for efficiency gains to control liquidity risk. Manual processes cannot provide the speed, visibility, and control needed.
RPA delivers on these fronts through automating repetitive, manually intensive efforts – from reconciling payments to collecting debt. And as part of a broader automation solution, RPA becomes even more powerful.
Already, leading financial institutions like Deutsche Bank and DBS are leveraging RPA to transform treasury operations. As liquidity concerns grow in 2024, expect RPA and automation to become prerequisites for any treasurer to thrive.
