Why is this important? Because there has been a question mark over the efficiency of systems based on multiple mixed ledgers for years. Companies are already combining customer data and analysing it to assist business plans and the advantages are evident.
CFO’s, financial directors and credit managers are well aware of the value that this brings both in terms of actionable insight and time savings. There are challenges involved in implementing a big data analysis system, but these have to be balanced against the increase in financial risk of continuing to use complex, often conflicting IT systems.
The first step, therefore, is to deal with existing, embedded 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 an all-round view of the customer, and even more significantly, how can that then be transformed into vital analysis?
The answer is to start with an achievable goal. 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 objectives that they can easily meet, data that can analyse trade payment behaviour, for example. This delivers an overview of the characteristics of customer payments that enables credit managers to form a case-by-case view on credit limits and access. It puts them in the driving seat and it gives them the tools needed to bring silo departments together, to collaborate and combine efforts for optimum return.
It also benefits 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 outcome is that big data projects are seen to have real value and those invested in the process achieve board-level endorsement to extend them more broadly.
Silos exist in companies because individuals and departments lack the appropriate mechanisms and incentives to share data – company culture has inadvertently developed to keep information separate. Credit managers however, by the nature of their role, rely on the sharing of data, whether it’s with sales departments, back office functions or in fact anywhere in the order to cash cycle. Their priority is to shed the old, restrictive culture and instead establish 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 it has been brought together, 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 “days sales outstanding”.
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.
Optimise risk transfer. Improve management through exchanges with business partners, including credit insurers.
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 has the potential to deliver visibility across the entire organisation and allows questions to be answered in all departments 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 providing a detailed long-term picture of a customer’s payment behaviour to facilitating analysis that can help in asset recovery, big data removes the guesswork for credit managers, CFO’s and financial directors, builds an accurate picture of the financial opportunities and pitfalls and allows organisations to manage risk according to their own particular appetite.
Sebastien Clouet is marketing director of Tinubu Square.
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