Treasury Today Country Profiles in association with Citi

Data analytics drives efficiency gains

Companies are increasingly looking to data analytics and business intelligence tools to help increase efficiency in treasury processes. We give an overview of the role data analytics can play in treasury, the types of solution available and considerations to bear in mind when choosing a solution.

At a time when many companies are looking to reduce their IT spend, data analytics can offer clear benefits in terms of reduced costs, better forecasts and improved decision-making for critical treasury functions.

Defining data analytics

Data analytics involves examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organisations to make better business decisions. It is different from data mining, in which data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships.

From a treasury perspective, data analytics involves taking the raw data that a company generates and using advanced models and data collation tools to pull out useful information for treasury. It encompasses business intelligence, business performance management and data integrators. It also employs a variety of other technologies that involve taking large amounts of data, pulling out the data relevant to a particular process, and analysing that data to improve process efficiency or the effectiveness of decision-making.

Data analytics can include modelling, reporting, analysis and score carding. Companies can use data analytics tools to analyse process efficiency, identify and respond to business or market trends and improve risk identification and mitigation.

Data analytics in treasury

One area where data analytics can be a big boon for treasury is in the realm of cash flow forecasting. Achieving an accurate forecast requires bringing together data from a variety of both internal and external sources. These data sources may have different formats and different levels of consistency.

Data analytics and business intelligence solutions can bring this together into a single format for trend analysis and to provide tools to cut the data in a variety of ways, depending on the needs and wants of users. For forecasting, such solutions can integrate historical A/P and A/R data, information from banking partners, exchange rates and other market factors, customer behaviour information, and so on, to build a complex forecast of cash flows. This goes beyond static historical data to incorporate events affecting the market, rates and vendor and customer behaviour to create a sophisticated model for forecasting.

In addition, companies can use data analytics for improving decision support for borrowing, hedging and investment strategies. Data collators can pull information from external sources – such as historic fund performance or interest rates – plus historic and current internal business information, which can then be put through advanced analytics solutions to provide a rich picture of existing and future conditions that can be used to make better-informed decisions in these areas.

Further applications

Beyond the basics, data analytics can be useful in improving functions that are of increasing importance to the treasurer, but may fall outside straight financial management – such as bank relationship management.

Data can be pulled out of bank systems and trading platforms, for example, to model why and when a bank won business, and why and when it did not. At present this would involve some manual involvement to bring all of that data together, at least in terms of telling those systems what data to pull and possibly getting it in a form that can be used by the analytics system.

This type of analysis can be invaluable during relationship meetings with banks. It can help a treasurer to negotiate tighter ancillary business requirements and possibly bargain for better fees on current business.

One corporate treasurer at a mid-sized company explained that the company collates data on foreign exchange, bank deposits, asset management and bank transfers, together with anything else that can be evaluated relating to a particular banking relationship. “I am keen on sharing business proportionally between our core tier-one banks,” says the treasurer. “Then by analysing the data, I can look at discrepancies or variances from targets set with that bank, and we can justify why they might have missed out on some business.”

Project steps

There are a number of steps which must be followed by companies setting up a new data collation and analytics project:

  • Decide the specific data sources that drive the process the company wants to analyse.

  • Assess the quality of data sources that will feed the analytics.

  • Create the IT architecture to deliver the model – which potentially includes buying the hardware and software to handle data integration, manage a data warehouse and host the analytics application(s).

  • Create the set of rules that will be used by the platform to pull source data and transform it into a usable format for the analytics application(s).

  • Implement the data analytics project and test the validity of the results.

If driven by treasury, clearly any such project will require both treasury and IT resources to prepare and implement. Innumerable other internal and external units could be involved as the relevant data is tracked and integrated – such as A/P, A/R, interest rate and foreign exchange management, other finance functions; CRM; pension management; data from the physical supply chain; shared service centres; payment factories; and data drawn from third party systems.

It is critical to have such a project driven from above in order to ensure that all those involved in the project – or those whose functions touch on the project – are fully committed to seeing it through.

Range of vendors

Data analytics tools are available from a number of sources. Treasury management system providers are increasingly integrating such tools into their platforms. For example, SunGard offers a bank account compliance and fee analysis solution to help companies get a view on bank compensation – producing reports on bank services, fees and compensation practices, and allowing users to track trends and differences.

In addition, ERP providers – such as SAP and Oracle – have taken business intelligence tools already designed for other market segments and adapted them to treasury’s use. For example, SAP has Business Objects – which uses a data analytics platform to provide forecasting, planning, risk management and financial reporting, among other things. Oracle also has a number of solutions geared at data analytics, including its PeopleSoft Analytic Applications and Oracle Data Integrator.

Then there are the solutions on offer from the big software and hardware houses – such as Microsoft and IBM. IBM, as an example, offers Cognos for business intelligence, financial planning, business performance management, forecasting and other analytics.

Finally, companies can look to smaller vendors for more specific solutions, such as White Birch Software’s cash flow forecasting product or the Prophix Data Integration solution.

Choosing a solution

The particular solution a company will choose depends on whether they can leverage existing systems – such as business intelligence software being used for CRM functions – to analyse the specific process, what they are willing to invest in the project and what exactly they hope to accomplish with the project.

Even those companies that are reducing their IT spend are looking at data analytics projects. Using such solutions across treasury, in conjunction with other functions, can provide a transparency of processes that was not previously available. At a time when most companies are looking to increase efficiency in all processes and make better use of internal liquidity, data analytics can help to enable this.

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