Jose Franco, EMEA Product Executive, Treasury Services, J.P. Morgan:

Technology innovation is reshaping financial services. As a result, the value proposition for payments is expanding and evolving. Banks are transforming how they engage with companies, what solutions and services they deliver, and how they deliver them. The expanded value proposition is helping corporate treasurers provide a new level of value and bottom line impact.

Big Data is critical to this. Technology enables banking partners today to analyse vast amounts of data aggregated from across diverse sources, synthesise the data, identify patterns, and distill meaning to support corporate treasurers with actionable insights. Data intelligence can enhance tactical and strategic execution and when used effectively, will drive improvement in the underlying business model.

Getting better visibility on cash flows is one well-known aim of digitisation but, importantly, new digital payments channels for consumers such as e-commerce and mobile are offering treasurers the opportunity to get far richer insights. Treasury will have greater and timelier access to vast quantities of data and more importantly, a deeper understanding of what that data means.

This data will allow treasurers to become much more strategic in creating actionable insights for their business. One example is delivering better forecasting on a real-time basis as the timeliness of payments and collections improves, with “track and trace” expanding the visibility of payments and collections. In the card space, mining spend data obtained from the business can be married with payments data obtained from your department or bank to surface insights that can be used to develop compelling offers for cardholders that will increase loyalty and revenues.

Some practical applications of Big Data include:

• KPIs

  Corporate treasurers get a lot out of peer and performance benchmarking. This can include points of efficiency such as a comparison of a company to industry STP rates or average transaction costs.

• Fraud detection

  Identification of unusual activity or patterns can help to uncover and prevent fraud attempts. This has tremendous value in an environment of increasingly sophisticated cyber threats and fraud.

• Analysis and working with banking partners

  Payment processing, managing idle balances and overdrafts, transaction costs and FX exposures are just a few of the many areas of information that can yield intelligence to improve treasury practices.

Building for Big Data means building for flexibility and future needs. It is important to consider the value of data points, the frequency, format and application. The data itself also needs to achieve a specific treasury objective, which remains focused on maximising shareholder value by optimising capital efficiency and managing liquidity, currency and economic risks.

Olle Malmgren, Executive Director, Treasury Solutions Americas, Transaction Banking, Standard Chartered:

Corporates generate and store huge amounts of data about their business flows and it is increasing exponentially. This data comprises everything from structured data in databases to unstructured data such as video and pictures.

The challenge in the past has been the ability to process large volumes of data from multiple sources quickly for timely informed decision-making. However, a huge reduction in data storage costs, powerful analytical tools and artificial intelligence is making it possible to not only mine huge pools of internal and external data but to also define cause/effect relationships across elongated business processes which involve multiple business functions and internal/external stakeholders.

There are a number of areas where the treasury could benefit from more timely, accurate and comprehensive data, especially when artificial intelligence is added, such as behavioural analytics

The most obvious area would be forecasting, where more accurate information about payments and receipts in near real-time would significantly help the treasurer to plan funding and exposure management.

Big Data could also combine the data from both the financial as well as the physical supply chain, giving the treasury a whole new dimension of information. This, in turn, could help in the cooperation between treasury, procurement and sales so that the treasury evolves from just managing the funding needs and exposures created by the business to proactively working together with them to optimise these functions.

Treasury can work with the supply chain organisation to optimise working capital management by using Big Data to increase visibility across the supply chain. It can help the supply chain organisation to optimise procurement, inventory management and distribution based on real-time supply chain information.

For example, treasury can proactively manage expected bottlenecks in working capital to speed up the release of goods to distributors/buyers or provide additional finance to suppliers who need additional working capital to increase production of critical components/products. Of equal importance, the impact on sales revenue or time to market can be quantified and measured in terms of ROE on the additional capital deployed.

The supply chain organisation can also more quickly respond to changing demand levels, reducing the days of inventory outstanding (DIO) and improving the cash conversion cycle (CCC).

Other areas where Big Data could add value is on counterparty risk management, where behavioural analysis could help the corporate to detect buyers that might become bad credit risks. Today’s tools are capable of relating subtle changes to buying and payment patterns to increased levels of financial stress or default.

Big Data has also been used to detect fraud in the business. This can be fake invoices for example that data analyses comparing income with liquidity could detect.

In general, Big Data can deliver better trend analysis since the mix, breadth and amount of both real-time and historical data that can be covered is much greater than a TMS can cover.

In order to become part of any business-wide Big Data initiatives, treasury must clearly define the outcomes they wish to achieve from the process and what data is needed in order to achieve expected results. The list could include; expected information, reason for the report, benefit of knowing what data should it be using, output format, accessibility and priority.

It is also important to make sure the data storage, analysis and sharing is fulfilling ethical and regulatory requirements.

Big Data could well be the enabler that will allow treasury to become the truly strategic business partner that so many treasurers aspire to.

Steffen Diel, Head of Treasury, SAP:

Leveraging Big Data can add significant value to treasury departments, but there are numerous challenges to unleashing its power. In a volatile environment, treasurers need broad insight – historic, predictive and real-time – to take appropriate decisions. However, the structured storage, access to and analysis of large amounts of data from a vast number of sources present technological obstacles that need to be overcome before one can explore new opportunities.

In treasury, we are consuming all kinds of structured data from various data sources, not limited to a single system environment. Information is derived from ERP, a treasury management system, a Business Warehouse, FX Trading Platforms, eBanking systems and many other specialised systems. Every application has its own structure of data and that makes it difficult to create a single source of data that one can analyse for smarter and faster decision-making.

Moreover, we might want to add the analysis of unstructured data to our current approach that relies on structured data like forward rates or GDP and unemployment rates. So, the question is, what is the technological foundation for a valuable use of Big Data?

SAP has created the HANA Platform which is based on in-memory technology. Bringing together transactional and analytical data in one in-memory database enables a very fast data insight and on-the-fly scenario analysis. The platform also acts as a data warehouse, integrating data from a wide variety of sources that may come from inside the company or outside.

Based on such a platform, treasury can achieve better control and visibility over the entire treasury-related value chain. The data consumed can be expanded to the complete portfolio of company data and external data can equally be added to the analysis. This combination is instrumental to help manage the exposure of a company and to enable new dimensions of business insights such as:

• Cash management – improving cash flow forecasting by leveraging historical data in combination with actuals and planning data resulting from various sources to improve forecasting accuracy. As an additional element, payment and buying behaviour can be added in order to perform predictive planning and ‘what if’ scenarios.

• Analysis of unstructured data derived from social media can be used as trend or early warning indicators for market developments, eg currency movements. Analysis of unstructured data received from information providers can be combined with structured data, eg counterparty risk management, to allow for a more comprehensive picture of what is happening.

• Identification of patterns where business data correlates to certain financial outcomes, ie country-specific data that correlates with the development of days sales outstanding (DSO).