Demystifying New
Technologies in Treasury

Much has been written about how technology is transforming the treasury industry, but the reality is that many companies have yet to fully harness the power of emerging innovations in cash management.

Research has shown that while more companies are looking to embrace technology to enhance treasury processes, only a fraction has been successful in reaping the full benefits. The reasons include legacy systems, inertia to change, fear of disruption and complexities in change management.

These challenges are exacerbated in treasury functions which typically do not get sufficient IT resources and have licensed-based treasury management systems and ERP platforms that are expensive to build and costly to upgrade.Lack of awareness also plays a part, with many treasurers unaware of the myriad emerging technologies available to them.

But as the treasurer’s role continues to evolve and expand in a rapidly changing environment of macro-economic events and regulatory developments, practitioners are recognizing that leveraging technology has become necessary for their function to deliver more with less. With the cost of new technologies falling, it also makes financial sense.

This article examines where treasurers are in this transformational journey and attempts to demystify the new technologies that will define the future of treasury management.

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Where we are now, where we are headed

There are three key areas where technology is making a clear difference in the treasury space:

Batch processing to Real-time processing

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Currently, the exchange of information – which includes balances, payment status, foreign currency rates – between banks and their clients predominantly takes place in periodic batches. Thanks to application program interfaces (APIs), this process could eventually happen in real time. Blockchain technology, when commercially applicable, will further enhance the flow of information by ensuring that the data shared is immutable and secure.

Task-driven to Intelligence-guided activities

Spreadsheets have been a core tool for treasurers over the past 30 years, used primarily for task-driven activities such as data gathering, processing and reporting. But it's a platform that’s largely manual and prone to human error. Enter robotics, which is increasingly being deployed to take on these mundane and repetitive tasks, and doing them faster and more accurately. Adopting sophisticated tools like machine learning adds intelligence to the mix, helping treasurers with forecasting, fraud detection and even investment decisions.

Physical systems to Virtual platforms

Aside from the challenges of overhauling legacy systems, treasurers have cited the risk of intellectual property theft, privacy and data loss when considering moving to cloud. But better data encryption has gone some way in alleviating these concerns. Together with falling implementation costs, cash managers are warming up to the prospect of using virtual platforms, recognizing the range of benefits they bring, which include lowered costs, improved scalability and business continuity.

New technologies and how they are being deployed

Treasurers can harness new technologies to achieve their operational and strategic objectives of optimizing capital, increasing operational efficiency and mitigating risk. We feature five emerging technologies that are already bringing value to the treasury function.

Finance and Treasury

CLOUD-BASED ERP AND TREASURY MANAGEMENT SYSTEMS

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Front Office

Machine
Learning

RPA

APIs

Cash forecasting

FX execution

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Middle Office

RPA

APIs

Blockchain

Intercompany netting

Treasury accounting

SWIFT GPI

FX confirmation

Information sharing

Trade finance

Payments

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Back Office

Machine
Learning

RPA

Fraud/error
detection

Reconciliations

Data retrieval

Reporting

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Front Office

Machine
Learning

RPA

APIs

Cash forecasting

FX execution

Shape Created with Sketch.

Middle Office

RPA

APIs

Blockchain

Intercompany netting

Treasury accounting

SWIFT GPI

FX confirmation

Information sharing

Trade finance

Payments

Shape Created with Sketch.

Back Office

Machine
Learning

RPA

Fraud/error
detection

Reconciliations

Data retrieval

Reporting


Five Emerging Technologies

What In a nutshell How it's deployed
Robotic Process Automation (RPA) Intelligent tools that automate tasks that humans currently perform RPA can be deployed across treasury’s front, middle and back offices.

It’s applicable in obtaining real-time price quotations and executing FX transactions, creating accounting entries for intercompany netting and treasury payments, preparing bank account reconciliations as well as identifying and verifying information in payment advice emails for cash application.
Cloud Means of storing data and accessing computing services over the Internet Cloud is applicable across the front, middle and back offices, through cloud-based ERP platforms and Treasury Management Systems.
Machine
Learning
Intelligent tools that can learn and make predictions of data Machine learning can be deployed in the front office for forecasting cash receipts / payments and working capital requirements, and in the back office to detect payment fraud / anomalies.
Application
Program
Interface (API)
Software programs that transfer data between two applications on demand APIs can be leveraged to streamline FX execution / confirmation and to obtain real time visibility into payment status and cash balances.
Blockchain Shared digital ledger that facilitates secure transactions accurately without a central party Still in the early stages of commercial adoption, Blockchain has the potential to make the most impact by delivering efficiencies in information sharing, trade finance and payments. As a transparent system, Blockchain can simplify trade finance processes by connecting all parties involved round the clock, as well as enabling settlement of domestic and cross border payments in real time.

Robotic Process Automation (RPA)

In a nutshell

Intelligent tools that automate tasks that humans currently perform

How it's deployed

RPA can be deployed across treasury’s front, middle and back offices.
It’s applicable in obtaining real-time price quotations and executing FX transactions, creating accounting entries for intercompany netting and treasury payments, preparing bank account reconciliations as well as identifying and verifying information in payment advice emails for cash application.

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Cloud

In a nutshell

Means of storing data and accessing computing services over the Internet

How it's deployed

Cloud is applicable across the front, middle and back offices, through cloud-based ERP platforms and Treasury Management Systems.

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Machine
Learning

In a nutshell

Intelligent tools that can learn and make predictions of data

How it's deployed

Machine learning can be deployed in the front office for forecasting cash receipts / payments and working capital requirements, and in the back office to detect payment fraud / anomalies.

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Application
Program
Interface (API)

In a nutshell

Software programs that transfer data between two applications on demand

How it's deployed

APIs can be leveraged to streamline FX execution / confirmation and to obtain real time visibility into payment status and cash balances.

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Blockchain

In a nutshell

Shared digital ledger that facilitates secure transactions accurately without a central party

How it's deployed

Still in the early stages of commercial adoption, Blockchain has the potential to make the most impact by delivering efficiencies in information sharing, trade finance and payments. As a transparent system, Blockchain can simplify trade finance processes by connecting all parties involved round the clock, as well as enabling settlement of domestic and cross border payments in real time.

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How J.P. Morgan is leveraging new technologies today

J.P. Morgan is committed to investing in innovative solutions to deliver value added services to its clients.

CASE STUDY: Applying machine learning to optimize liquidity

Headquartered in the U.S., the client is a global technology company that manufactures computer hardware and software and has operations worldwide.

The situation

  • After acquiring a company, the client inherited subsidiaries across Asia that were keeping excess cash and without accurate means to forecast day-to-day working capital requirements
  • As the company was leveraged, it wanted to extract excess cash to reduce external debt and associated financing costs

The J.P. Morgan solution

  • J.P. Morgan applied machine learning predictive analytics to help the client determine the optimal level of operating liquidity
  • The neural network-based model, which uses algorithms to learn from data, was used to predict the client’s short-term working capital requirements
  • Based on the results, J.P. Morgan was able to determine the optimum level of liquidity and deduce the target balance to be maintained in the client’s regional cash pool

The results

Improved forecasting

Client’s operating liquidity requirements were determined with increased accuracy

Operating liquidity determined

Results showed that on a combined basis, the client could release $2.5 billion in cash

Reduced financing costs

Excess operating liquidity utilized to repay external debt

Myth or Reality? Test your knowledge of new technologies.

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