This article examines the links between financial data quality and process automation.

High Quality Financial Data allows organisations to:

• Pay suppliers promptly
• Reduce bad debt and DSO
• Reduce operational costs
• Improve strategic decision making
• Increase compliance and reduce risk

For many organisations who are embarking on projects to automate processes in the finance department, (examples of processes to automate in Finance), the initial focus and attention is often on the characteristics of the technology offerings being considered.

Initial questions tend to centre on:

“What is the automated extraction rate?”

“How accurate is the software at reading data?”

“How fast can it process transactions?”

Whilst these are all valid questions, the reality is that in most cases the answer will be heavily dependent on the quality of internal data sets. Process automation is heavily reliant on being able to lookup, validate and crosscheck information throughout the process. So, to understand how successful a piece of technology may be at automating steps in a process, consideration first needs to be given to the accuracy of information in the business.

What is Data Quality?

Data quality is a measure of its condition and suitability for the purposes it is intended to be used for. Attributes like reliability, accuracy, completeness and whether it is up to date are all critical. Data quality is important from a number of perspectives as it doesn’t just affect the efficiency of processes, but can impact strategic decision making and affect compliance with internal and external regulations and policies.

Why is Financial Data Quality Important?

Most finance automation projects will utilise existing information already held on internal systems within the organisation.

If this data is inaccurate or incomplete, then this can lead to challenges when it comes to automation. Potentially this can then increase the number of transactions needing manual assistance through an exception process or, worse still, processing errors.

Examples of Finance Data

Finance processes are very heavily reliant of existing data sets. An Accounts Payable Automation / Supplier Invoice Processing project will be reliant on Master Supplier Data / Master Vendor Data, Purchase Order Data and Goods Receipt Data (GRN). An Automated Cash Allocation / Cash Application project requires the use of open sales invoice and customer data. Inaccuracies in these data sets can delay the payment of suppliers, pay the wrong suppliers, chase customers for non-existent debt, delay receipt of payments, lead to a higher DSO (Days Sales Outstanding) and a higher suspense account balance.

Steps to Improving Finance Data Quality

There are 4 critical steps in improving data quality:

1. Benchmark

This stage is really about taking stock of the data that exists within the organisation already. This could be as simple as looking at a Master Supplier Data set and analysing whether there are duplicate entries, or missing fields which would help an automation project. For example, having a record of VAT numbers against suppliers can be very helpful when automating the processing of supplier invoices, but is not generally used or needed in a manual process. So, focusing on the accuracy and completeness of this field makes sense to maximise data extraction rates.

2. Improve existing data sets

Once data elements have been identified for improvement, the next step is to bulk clean up existing data. This may focus on individual fields or more widely as complete entities and all their associated fields.

3. Implement Data Quality Processes

In conjunction with cleaning existing data sets, focus needs to be given to processes that collect new pieces of information. So, as and when new suppliers are onboarded, the process should dictate to users the information that is required and validate that information as it is collected. Ensuring certain fields are mandatory and combining this with validation rules that check company details (address, VAT registration number etc) will ensure that bad data is not being added.

4. Monitor

Over time, processes and ways of working will change. As they do, it is important to measure improvement results to ensure that these changes have not lead to degradations in data quality.

Practical Applications of Data Validation for Financial Data Quality

Some data validations that will improve overall data quality are easy to consume as a service and can have significant positive impact to automation rates. Some of the most commonly used services in the finance function include:

Company Details

Matching company details to Companies House records can ensure critical information about suppliers and customers is accurate and kept up to date. Company name, address, VAT numbers, group structure, registered company can all be automatically validated.

Address details

Address details can be validated against the PAF (Postal Address File) to ensure that they match to standardised formats. This ensures addressed correspondence will be sent to the right place and helps to avoid duplicate records because of differing address formats. PAF validation can be applied to addresses already held in systems to clean existing data sets as well as applied at the point of capture.

Email addresses

In the finance world, more correspondence is now sent electronically via email than through physical channels. Ensuring email addresses are accurately captured and available can be challenging. With substantial benefits for migrating to this channel, due to speed of sending, lower costs and enhanced monitoring, getting email addresses correct is substantially worthwhile.

Automatically validating email address structures to ensure that they conform to set formats, checking for spelling mistakes in the username and domain, as well as verifying that it accepts messages all assist in helping to ensure important communications will land with the intended recipient.

Duplicate Checking

It’s not uncommon, that over time, systems will succumb to certain levels of data duplication. This causes issues for automated processes where the technology may be trying to match to an appropriate supplier or customer record and more than one record is found.

Often duplicates will occur as a result of changes of addresses, multiple locations for suppliers or customers or as a result of inexperience users adding details into systems. Using advanced data matching engines that quickly and seamlessly identify and merge records, it is a quick way to improve accuracy and potential for higher levels of automation.

Telephone Numbers

Contacting suppliers and customers by phone is often required for resolving disputes, payment issues or delivery timescales. By validating telephone numbers to check length and whether the number is callable, it becomes possible to increase the accuracy of data held and that users have the right information to hand.

Bank Details

Automatically validating bank account numbers, sort codes and IBAN details helps to avoid costly failed payments. By matching sort codes to account numbers incorrect entry is prevented at the point of entry.

Credit Worthiness

Credit checks are often performed at the point of entry, but are often not revisited for long durations after the initial assessment. This can mean that trading decisions could be being determined on incorrect financial positions. By automating credit assessments and revising credit limits accordingly allows organisations to make more informed decisions that protect the organisation from bad debt.

To discover how your organisation could benefit from improving financial data quality or automating finance processes you contact us here and one of our experts will be in touch.