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How New Finance Data & Analytics Tools Shape Credit Management

5 Technology Trends Impacting Finance & Credit Management Teams

You may not need a weathervane to know which way the wind blows, but your local meteorologists might beg to differ. They depend on data-driven models of atmospheric conditions to predict the weather, and recent technological advancements have enabled their profession to come a long way.

Credit managers can’t afford not to know when their accounts might pay late.
 

Just as meteorologists analyze weather models, credit managers depend on accurate datasets to manage credit risk. A credit professional who manually reviews credit reports to determine creditworthiness may only be making “partial predictions.” Most companies operate in complex financial landscapes, and robust analytics are needed to help make sense of all the different credit and risk scenarios. After all, credit managers can’t afford not to know when their accounts might pay late.

 

How are Finance Solutions Enabling Innovation & Growth?

Automation has redefined and replaced many manual workflows, and it seems the tradeoff is that now employees are expected to spend less time on transactional tasks and more time on strategic ones, according to our new eBook, Weathering the Storm: Forecasting How Data & Analytics Impact Modern Credit Teams. Analytics and data modeling allow for more predictive forecasting, but are companies heeding the outlook about the future of their business?

Last year we identified five best practices that CFOs need to implement in order to leverage data for business insight. Now, we take another look to see if the average financial professional has benefited from these investments in data and new technology. Have they been able to balance enterprise risk and business growth opportunity by prioritizing collections, reducing remittance errors, and making smarter credit decisions? Find out if these trends gave an accurate forecast for credit and finance teams in the eBook, which covers:

1 – Cloud capability that enables efficient access to and the sharing of data

Software giant Oracle also predicted the trend for cloud-based solutions to replace legacy systems that inhibited data sharing. Their study, Modern Finance in the Digital Age, found that “Modern CFOs are increasingly turning to cloud-based financial management systems to replace legacy ERP systems … and deploy new functionality to complement on-premises systems. Using the cloud enables CFOs to redeploy the capital they save on IT maintenance and hardware to fund new business opportunities, and reassign IT staff to work on technology-led innovations.”

Switching to a cloud-based solution can be more cost-effective, but it does have its security concerns, and some organizations are wary of moving away from an on-premise offering. Storing sensitive financial data on your company’s own servers is more expensive, but can make it less vulnerable to data breaches. Despite the security concerns, it does seem as though more companies are moving to the cloud. As such, the marketplace has responded to demand for cloud-based finance solutions – Oracle bought the cloud-based ERP NetSuite in 2016, and Sage bought Intacct in 2017 to better compete with cloud-based software from SAP and Workday.

2 – Robust cleansing and enrichment for data that is accurate, complete and always up-to-date

Data quality is a hurdle that many companies haven’t been able to overcome. Credit departments everywhere already rely on a mix of both internal and external (third-party) data from the major bureaus to determine creditworthiness. A 2017 survey by Forrester Consulting (commissioned by D&B) found that about three-quarters of CFOs use external service providers for data and analytics to fill in the gaps in their internal insight.

Of course, any data-related project can only be successful if the input is accurate and up-to-date. Dirty or decaying data makes for inaccurate analytics. You don’t want your reports showing all your best customers are located in the US and Germany because that’s where their headquarters are – you want it to show the branch location you actually do business with.

Another common data redundancy is having separate, disconnected accounts for customers that are actually part of the same corporate family. The same goes for separate account entries for the same company – think 7-Eleven and 7-11, or IBM and International Business Machines. You don’t want bad data to lead to bad decisions.

One of the best ways to build a strong foundation of data quality is to leverage an external data partner that can optimize your organization’s internal data. “Cleanse and append” is the industry term, and matching your data against another’s larger dataset of pre-mastered commercial content can help provide a valuable data structure.

3 – Powerful analytics that rapidly generate insights and answers from your data

Analytics has emerged as a key requirement for the modern credit team; it’s no longer just a “nice to have.” But the definition of analytics runs the gamut, and some say it’s just a fancy word for reports. According to Analytics Accelerates into the Mainstream, a study by Dun & Bradstreet and Forbes Insights, 70% of companies say at least half of their decisions are made based on analytics. Before you think these companies are using sophisticated predictive tools that cost a fortune, 40% admit their analytics consists of spreadsheets.

As for the finance department, 63% of CFOs said they leverage data and analytics to find opportunities to fund business growth, support long-term strategic planning, and contribute to revenue via sales acceleration. While these goals seem high-level, they report the analytics have resulted in fewer credit defaults and a quicker turnaround time for credit applications – which are direct, tactical benefits for the credit department.

The key here is not the method of delivering the analytics – whether it’s spreadsheets or special software – but the knowledge gained from interpreting the data through the analytics. If your team’s monthly reports just regurgitate the general numbers that management likes to hear, that could be preventing you from gaining any real insight. Drill deeper to unlock true value from the analytics.

4 – Automated processes that facilitate collaboration among departments

Automation is the other key requirement to facilitate innovation. A 2016 survey by Credit Today found that the benefits of automation are numerous and obvious – it saves time, money, and manpower to automate what are essentially clerical duties. According to the survey, companies will have less bad debt, more accuracy, greater predictability, and the ability to increase credit lines with automation. However, credit departments that are under significant budget pressure or make high-volume/low-dollar credit decisions might not feel compelled to invest in new technology.

But with automation, the credit manager transforms into a credit analyst. They’re in a unique position, with access to both pre-sale and post-sale data they can use to drive insights across the enterprise. It’s so much more than having software analyze your account data to alert you to past due accounts (although that’s a start). The credit or collections manager is still tasked with reaching out to those aging accounts – which could number in the hundreds. For instance, wouldn’t it be better to identify accounts that have the propensity to pay late, instead of just being alerted to past-due accounts? Then, the system would keep an eye on them before it impacts cash flow.

Automation has the ability to greatly streamline the collections operation. The aforementioned Credit Today survey on automation found that three out of four (77%) credit departments have automated at least part of their order-to-cash process. But like the Forbes study, the definition of automation varies, and being “fully automated” is still rare among credit departments. The survey found that, in fact, only two of the 12 steps of the order-to-cash process are automated, and they’re the most clerical – billing and invoice processing (EIPP). To be sure, there is still a way to go for many credit departments to reduce manual processes.

“At first glance that [77%] sounds pretty good. However, when you consider that the use of accounting or ERP software is almost universal in corporate America, the fact that 23 percent have not automated any part of the order-to-cash process is telling.”

There is also untapped opportunity for credit departments to collaborate with the sales team to help find and win new business. There is already a corporate movement underway to align sales and marketing – you don’t want there to be any disconnect or miscommunication between how a pre-sale prospect is treated by marketing versus how a full-blown lead is treated by sales (and even further, how a paying customer is treated by customer service).

The same holds true for finance and sales. The credit and finance departments have access to information that can uniquely help sales, and they can collaborate to help drive growth. For example, credit managers could analyze active prospects against a roster of current customers to find the ones most likely to buy, as well as their budget, and share that information with sales. This proactive client-matching and prospect-profiling could then be used to pre-screen prospects for risk and pre-approved credit terms.

5 – Dashboards and tools that are easy to use and present decision makers with insights in a digestible way

With all the data available today, visualization tools (such as dashboards) provide the ability to synthesize data and present it in a pretty package via charts and graphs. Pretty much every type of software has a reporting function that provide analytics, but is it easy for everyone to use, or is it so specialized that only one person handles the reports?

New plug-and-play analytics capabilities promise instant value, but D&B’s Wired Money report warned us to “Be cautious of falling into the trap of relying too heavily on automated dashboards; they might look nice, but you may be missing key insight that comes from taking time to address not just when or where something occurred, but why it occurred. The difference between a strategic dashboard and an aggregated data dump is that the dashboard not only tells a story, but offers actionable insights.”

A truly modern credit team doesn’t just use dashboards to report the facts, but for analysis as well. Not just for observation, but for understanding.

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