Businesses are leveraging big data to transform their processes and inform their strategic planning. The transformation takes time to implement but its effect is significant, not least in the finance department, where the efficacy of systems based on multiple mixed ledgers has long been questioned and found wanting.
The advantages of combining all the data that a company has on its customers and the analysis that can be drawn from the data is well documented. FD’s and credit managers understand the value that this brings both in terms of actionable insight and time savings. But they also have to weigh up the challenges of introducing big data analysis against the increase in financial risk of continuing to use complex, often discordant IT systems. The first step, therefore, is to deal with existing, entrenched processes and silos that still remain in many organisations. How can information that is fragmented within different departments, be combined into a single, useful function that gives a 360° view of the customer, and even more importantly, how can that then be translated into vital analysis?
The key is to start small. If manageable data and analytics projects in specific departments result in tangible benefits, the value of them to the company will increase. Credit managers should focus on easy wins, data that can analyse trade payment behaviour, for example. This provides an overview of the characteristics of customer payments that allows credit managers to form a case-by-case view on credit limits and access. It puts them firmly in control and it gives them the tools to bring silo teams and departments together, to collaborate and combine efforts for optimum return. But it is actually to the benefit of all operational employees, not just credit managers but right through to sales directors and CFOs to promote big data as a means to make good financial decisions. The net result is that big data projects are seen to have real value and those invested in the process gain board-level endorsement to extend them more broadly.
There is also the issue of company culture to consider. The reason that silos exist in many companies is because individuals and departments lack the appropriate mechanisms and incentives to share data. Credit managers, by the nature of their role, rely on the sharing of data, whether it’s with sales departments or back office functions or indeed anywhere in the order to cash cycle. Their priority is establishing a smart risk culture - a means by which their company can increase its tolerance to financial risk - and the best way of making this work is by gaining the involvement of the entire organisation.
Once collated, big data can be used to support functions across the credit management spectrum. Here are some examples:
- Analyse the debts. Ascertain what the priorities are and come up with solutions for dealing with persistently late payers. This will reduce DSO.
- Guard against unnecessary risk by monitoring debt collection records and implement regular assessments to ensure debts are being paid. This will reduce the cost of risk and risk management
- Integrate, streamline and simplify credit management processes, saving substantial time and operational cost for more consistent, timely and actionable buyer information
Find out what late payments are costing the business? Analyse overdraft fees, or the impact on cash flow of overdue payments, not just in the last few months, but over the last few years.
Market and sell only to ‘acceptable risk’ customers as defined by enterprise policy. The data will help to establish insight into payment behaviour, which will inform planning.
Gain visibility and control of trade credit risk in real-time. The combination of big data and systems that enable real-time analysis is powerful, allowing credit managers to make decisions based on hundreds of variables that are always up to date.
These actions will strengthen the financial position and enable companies to obtain short-term bank credit
Big data delivers visibility across the entire organisation and allows questions to be answered that then inform important business decisions. Armed with this level of intelligence it is easier for companies to assess whether a high-risk customer, for example, should be on the prospect list and focus sales efforts on the strongest, high value opportunities that will deliver fast, full revenue recognition. Sales teams will be tangibly more productive and the company will be focused on maximising profitable sales. Apart from enabling decisions to be made about which customers to trade with or extend credit to, this data can also be leveraged to guarantee bank credit and reduce borrowing costs.
From facilitating analysis that can help in asset recovery through to providing a detailed long-term picture of a customer’s payment behaviour, big data removes the guesswork for credit managers and FD’s, builds an accurate picture of the financial opportunities and pitfalls and allows organisations to manage risk according to their own particular appetite.
By Sebastien Clouet, Marketing Director, Tinubu Square