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IS YOUR CREDIT MANAGEMENT FUTUREPROOF?

By Marieke Saeij, CEO, Onguard

 

Covid-19 has forced the world to adapt to a new normal. For finance teams this means shifting some of their focus from long-term growth to chasing payments that were put on hold earlier in the pandemic.

Much of 2020 has seen businesses turning to digital transformation to ensure business continuity through the pandemic. With the current lack of certainty, finance teams must now focus on future-proofing their credit management. Having always been a key component, good credit management is now critical to business survival in these uncertain times. This must start with the right data.

 

Data is key to future-proofing

The advantages of being a data-driven organisation are increasingly recognised. Digital transformation projects to ensure this are well underway in many organisations, with over three-quarters (68%) of finance professionals in the Onguard 2020 FinTech Barometer stating their organisation is already undergoing digital transformation.

Data insights can help to reduce the days sales outstanding (DSO) and allow credit managers to create a better understanding of risk profiles. Identifying payment patterns from the data produces better risk analysis and the ability to anticipate trends. The finance team is more rapidly alerted to the first signs that a customer will not pay, for example. Staff can then step in to resolve the situation. Data analysis will also predict a prospective customer’s expected growth, chance of bankruptcy or payment behaviour. This is not a capability many organisations currently have without laborious manual methods.

With these insights, finance departments can better advise management at the strategic level, elevating their role within organisations. But finance professionals’ insights may also help other colleagues. For example, sharing risk information with account managers will allow them to better calculate whether or not to approach a customer for upselling or new business, which could make all the difference.

Yet despite all the discussion of digital transformation, most organisations still only use a portion of their available business data. This is as true in credit management as any other area. According to the Barometer, only 7% of executives think their own organisation is already data-driven. It means the focus in credit management, as in other departments, must be on exploiting an organisation’s existing data riches because this is the most efficient and cost-effective route to becoming data-driven.

 

Put your data before third party data

Businesses should start by using data from their own consumer base, such as their customers’ payment behaviour. This is not only more cost-effective, but risk profiles based on an organisation’s own customers can reveal more about future customers than data from other companies. The risk profile scores based on internal data will therefore have greater predictive value.

Marieke Saeij

External data can be expensive, as highlighted by McKinsey, but it shouldn’t be discounted entirely. Its use can strengthen an organisation’s own data resources, bringing a wider understanding of the market that makes for better decision-making. An organisation can combine internal and external sources as it evolves to best suits its needs.

The gains from this hybrid approach are tangible and come as enhanced sales, improved products, better finances and more targeted marketing, supplying a better service that boosts satisfaction levels and leads to improved relationships.

 

Automation and AI

Robotic process automation (RPA) automates the repetitive manual tasks in credit management that involve collection and collation of masses of data and divert skilled employees from more valuable work. Artificial intelligence (AI), however, is the group of technologies with more far-reaching potential, making smart use of all available data. It links everything from CRM and ERP system data, to all the cogs in the order-to-cash process. This includes linking accounts receivables management with data about customer acceptance and e-invoicing. AI integrates these processes, transforming efficiency and delivering new insights through its analytical power. For finance departments it will also link with recognised parties that provide credit information, as well as payment service-providers and an automatic payment processing solution.

AI’s predictive capabilities help minimise non-payment risk, support the forecasting of cashflow and advise on follow-up actions. This includes, for example, whether individual customers will respond better to phone calls, or when there is no alternative to commencement of collection proceedings.

Using individual insights based on consumer history, AI can even help identify the best time to contact specific customers. This will this dramatically improve operational efficiency and if customers are approached in the right way, at the right time, will enhance relationships and bolster retention.

 

The personal touch

Although the future of credit management will hinge on the right technology, the importance of personal relationships must not be neglected. A future in which all contact with customers is automated will soon become unprofitable in credit management, where personal relationships are all-important.

Although data provides insight into overall payment patterns, it does not reflect the totality of the relationship with the customer. A credit manager might know that a single call is all it takes to trigger payment from a certain customer. Yet as much as AI will achieve, it still lacks the emotional intelligence to pick up on these kinds of nuances and subtle differences in character that make a difference. This matters because customers will soon switch providers when service-levels drop or if they start to feel they are just being treated as a number.

One of the ironies is that if an organisation has the right credit management solution, it will understand more about the customer and have a firmer basis for effective person-to-person interaction. If you know more about a customer, saying the right things to obtain the outcome you want is easier.

 

Looking ahead

It’s clear that the future of credit management will be driven by data. Data insights drive far better decision-making and outcomes, providing organisations not only with an edge on competitors, but also the agility to survive a fast-moving landscape.

Agility is key now more than ever. The last six months have shown that organisations must be prepared and ready to meet the challenges with credit management that is already future-proof. That requires becoming data-driven and the adoption of fully-tested automation and AI.

Yet, as we have explored, reliance on technology alone is not enough, and will lead to one-dimensional relationships. The personal touch continues to be important to building long lasting customer relationships, particularly in fraught times.

Alongside the implementation of solutions that deliver results quickly and cost-effectively, organisations need to embrace this hybrid approach that blends the best of conventional methods whilst preparing them for the data-driven future.

 

Business

BACK TO SCHOOL – CEOS NEED TO LEARN A NEW LANGUAGE, FAST!

By Simon Axon, Financial Services Industry Consulting practice lead in EMEA, Teradata

 

Chief Executive Officers of banks know all about change. Leading responses to new challenges, new opportunities, new regulation and new markets is all in a day’s work. But the existential challenge posed by Big Tech requires a totally new set of skills. It is an entirely different beast that inhabits a totally new environment and speaks its own language. CEOs now need to learn the language of data to survive in the emerging digital world.

Learning a new language later in life is hard. CEOs need to fully commit to accomplish it. Becoming data literate means mastering the basics of vocabulary and grammar. Gartner defines data literacy as the ability to read, write and communicate data in context, including an understanding of data sources and constructs, analytical methods and techniques applied — and the ability to describe the use case, application and resulting value.” Extending the language analogy: the building blocks are an understanding of logical data models – the basic vocabulary; meta data providing rules and information about data is the grammar.  Learning needs to go beyond parroting a few key phrases and acronyms. To really communicate in this new language CEOs must not only be data literate – but data cognitive. Language shapes thinking, and to succeed, today’s CEOs need to think data like digital natives.

Simon Axon

As anyone who has learned a language will recognise – practise makes perfect. This means rolling up your sleeves and getting into the data ‘lab’. Run some queries, experiment with data to test theories and learn how data can, and should, inform all aspects of business management. It is daunting, and different functions are fiercely protective of their data. But that’s one of the big cultural shifts the CEO needs to lead. Data is more valuable when it is used across the business. Developing safe and secure ways to combine, refine and analyse data at an enterprise level is fundamental to competing with Big Tech. The Chief Data Officer can help. Spend time with them and use them as a teaching-resource to get more familiar with what can and cannot be done with your data.

As you practise you will build confidence and move from school-level conversations to business-class data fluency. Spending more time looking at and working with data and you will begin to recognise ‘quality’ data, identify attributes and flag anomalies. This will build confidence and essential trust in data. Last year KPMG found just 35% of CEOs trusted the data in their organisations. This shocking stat undoubtedly stems from a data skills deficit among CEOs themselves. If they don’t know what to ask for, and can’t recognise what they get, they won’t trust it. To stretch our linguistic analogy, if you are not confident in the language then you’ll be anxious ordering food in a restaurant!

Ultimately, no one expects the CEO to personally implement data-analytics programmes across the business. But unless they have the confidence and the skills to accurately communicate what’s needed, to sit at the head of the table and ask the right questions about the menu, then the organisation is unlikely to put the right emphasis on the data strategy.

In How Google Works, former Google Chairman Eric Schmidt outlines how every meeting revolved around data – it is simply how Big Tech works. Banks need to adopt the same approach. Exploiting data in all scenarios must become second-nature. By modelling the use of data across the business – dissolving silos rather than sticking to narrow data sets that reinforce them, the CEO can define a powerful data culture. Operationalizing data strategy will, just like using language skills, stop data literacy from becoming rusty.

Entering any new market requires investment in understanding the language, culture and business environment. In the Big Tech world, data is the lingua franca informing every decision. Bank CEOs need to learn from them and invest in building their knowledge to become data fluent. There are no short cuts. Throwing money, bodies and tech at the problem will not get you there.

 

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Business

REVITALISING THE TOKEN MARKET

By Gavin Smith, CEO at Panxora

 

With interest rates near zero and fears that whipsawing stock markets are set for further plunges, many investors are turning to alternative markets in the search for returns. Money flowing into cryptocurrency hedge funds and trusts like Grayscale is at all-time highs and the large cap coins seem to be entering a bull phase, but that capital is not trickling down into new token projects. Why are blockchain token projects struggling to attract funding?

 

Seed investor scepticism

Setting aside the reputational issues with mainstream investors, even those educated in blockchain tech are not signing on the dotted line. This is certainly due in part to the hangover from the early token market.

During the heady days of 2016/17, investors could buy tokens during the token sale, and if the project was legitimate – even if the business case wasn’t particularly strong – prices would soar based on market enthusiasm. Early investors purchased at a discount and cashed out almost immediately for a handsome profit – and then repeated the process again. The token sale allowed founders to amass a war chest large enough to finance the entire token project – without having to give up a large chunk of company equity. Everyone got what they needed out of the deal.

Running a token sale is far more expensive today than it was during the boom. Getting the attention of the token buying public in a market where advertorial has replaced editorial is expensive. This coupled with a regulatory framework that requires the advice of accountants, solicitors and information gathering of KYC details for investors all comes with an escalating price tag.

To accommodate the change in cost structure, tokens now need to acquire funding in two rounds. Frequently there is a first round where capital is raised from a few, large investors. This cash is then used to finance setup and marketing the main token sale. The token sale, in turn, provides the capital needed to run the entire business project.

 

Bridging the gap between token projects’ needs and early stage investors

To successfully get a token through the capital raising process, founders must acknowledge the risk assumed by those very early investors and reward them appropriately. And given that tokens may stagnate or fall in price post token sale means that a deep discount in token price is not necessarily attractive enough to get investors to commit.

Many tokens have turned to offering equity in the business in the effort to raise that first tranche of capital. If you look at the number of successfully concluded token sales, the downward trend has continued since Q2 2018, so offering equity is not sufficiently stimulating the market.

 

Two sides of the coin

So, what is the answer? It’s a complex question but one thing is certain. Any solution must be rooted in a deep understanding of what both parties need to successfully conclude the deal.

On the one hand, token founders’ needs are clear: they need enough capital to get the token ready for and through a successful liquidity event that will provide sufficient funds to build the project. The challenge lies in striking the right balance between accruing that capital and making sure not to offer so much project equity that give up either the control or the incentive founders need to drive the project forward.

On the other hand, while the needs of the seed capital investors are more complex, there are two areas of key concern: transparency and profit incentives.

 

Transparency can mean many things, but almost always includes providing more informative cost and profit projections, as well as answers to a whole range of questions, not least the following:

  • What happens to investor capital if the token sale event fails? Token founders must be transparent from the outset. The token market is highly speculative and early investors run the risk of losing their money should the project fail. Therefore, investors require a well-established fund governance process in place throughout the fundraising so they can make informed decisions on whether the project is worthwhile. 
  • How are the assets for the entire project managed? Investors need to know that their money is in good hands and that proper treasury management techniques are being used to manage cryptocurrency volatility risk. Ideally, an independent custodian will be used to hold the funds and limit founders’ ability to draw down the capital – releasing funds to an agreed-upon schedule of milestones.
  • How are the rights of investors protected, for instance in the case of a trade sale? Investors need to know what happens if the company they are investing in is sold. What impact could this have on the value of their stake? Would a separate governance framework need to be established? These are critical questions and investors aren’t likely to settle for any ambiguity in the answers.

Profit incentives are important when it comes to encouraging early participation in a project. Investors need convincing that the proposition will keep risks to a minimum and focus on providing a strong probability of a return. This means that founders need to be able to defend the case for the increase in the value of their token.

But this isn’t the only incentive that matters. Investors can also be incentivised by preferential offerings such as early access to projects and services that might help their own business.

Let’s not forget that investors don’t support just any project. What really matters is that there is something special and unique about the business being underwritten by the token. Preferably something that could be shared upfront and directly benefit the investor – proof that the investment is really worth it.

And that’s what it all comes down to. Ultimately, while token projects are having a hard time finding funds at the moment, if they can prove their worth and provide full transparency and clear profit incentives to ease investors’ concerns, the money is out there. And deals can be done.

 

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