Future of Digital Finance
Digital finance is an emerging concept in many companies where today's Executive Board are saying that they want the finance function to provide real-time, data-enabled decision support analytics. Finance leaders themselves say they want to spend more time on digital commercial initiatives and the application of digital technologies to finance tasks. Reality is, Finance still spend less time on digital trends than they do on traditional finance activities. Consequently, the digital revolution has now become a business strategy as opposed to an IT strategic enabler.
Where can Digital Finance start?
Well there are four areas of technology, I believe that show the most promise for use in finance:
Much of the above will depend on the company’s starting point—its current strategies, needs and capabilities, existing technologies and finance staffs skill set. It is important to note that digital transformation will not happen all at once and companies should not use their legacy ERP system as an excuse not to start the change. By working on small pilot projects and successfully digitising the most critical tasks within finance will help ease the eventual rollout of bigger digital technologies across the entire function and/ or across other parts of the company.
Robotic Process Automation or Cognitive
A critical tool that a leading-edge client is already exploring and using is robotic process automation (RPA), a category of automation software that performs redundant tasks on a timed basis to ensure it is completed quickly, efficiently and without error. To successfully implement RPA at scale requires altering the target operating model and redesigning the processes. Refer to my article on “How to do a Finance Transformation properly”.
Cognitive automation is the next step after RPA. In this phase of automation, we are transferring cognitive functions from a person to a system or a robot. You train the system or robot in a more advanced way to process and respond to information as a person would. Good examples of cognitive automation are the classification of incoming emails. Based on the classification, you then hand tasks over to RPA solutions to fully automate the solution using natural language processing engines.
Examples of solutions that provide this are the Cogito Platform, IBM Watson and ELIS. Furthermore, the use of chatbots is increasing too. They are mainly used in customer service environments or in procurement to answer simple questions from customers or vendors. They are also used in accounting, but mainly for knowledge management. In this domain, a lot of suppliers are providing chatbots as a service. One of the main challenges in cognitive automation is the availability of good-quality data needed to train the system/bot.
Improving performance via data visualisation
A client I worked with was pairing automation capabilities with data-visualisation technologies, to create clear, timely, actionable business reports. These reports quickly pushed data to end users and presented data in intuitive formats that encourage focused business discussions. Rather than wait for reports, front line staff used visual dashboards (accessible from a laptop or mobile) to get the data they needed when they needed it—be it region, business unit, function, or other parameters. It involved pulling data from a central repository that is continually refreshed, so they can illicit on demand data. This self-serve now approach decreased the need for the finance group to generate reports by more than 50 percent and cut the cost of reporting by 40 percent.
Moreover, an executive board at a utilities plc company no longer uses PowerPoint. Business leaders instead use large touch screens to access real-time data about finances and operations. The information is presented in easy-to-read graphs that highlight deviations from plan. The graphs are dynamic, redrawing themselves as users swap variables in and out.
Finding value through advanced analytics
Companies are increasingly mining troves of business data (on people, profits, processes and so on) to find relevant insights that can improve business leaders’ tactical decision making. Similarly, the finance function can use advanced analytics to manage standard financial transactions and core processes more efficiently to shape and accelerate tactical discussions to identify broader ways of applying advanced analytics to uncover new sources of business value and trend.
I worked for a business intelligence group that used its own transactional data to data mine/ correlate traveller profilers, covering demographics, age groups, travel preferences, next trips abroad, shopping lists, preferences, etc… Using targeted marketing via mobile/ app technology the group would offer incentives (like discounts/ promotions) to make them visit “branded/ luxury” shops to avail also their VAT refund as foreign travellers' (depending on their residency).
Standard transactions
A listed property client used advanced analytics to monitor general mortgage submissions where it views this metric as an early indicator of its own sales.
Finance teams at other companies are using advanced analytics to identify duplicate expenses and invoices or to connect the terms of procurement and payment schedules for a good or service with actual invoices so they can spot early or missed payments or opportunities to apply discounts.
Core finance processes
A client retail-pharma company uses advanced analytics to improve its demand forecasting. Traditionally, its forecasting models relied on basic, internal customer data and used historic trends to predict future demand. Furthermore, the forecasts were at an aggregate level—that is, for entire classes of product rather than individual ones. The company cross-referenced internal customer data with external data sets, such as stock prices, revenues, weather, exchange rates, and business-cycle indices, to generate forecasts for specific regions and SKUs. In this way, the company could examine whether existing forecasts were accurate or not and react accordingly.
Tactical discussions
A UK consumer health company is exploring the use of advanced analytics in better predicting sales-volume changes associated with pricing moves for certain SKUs. I assisted the company with building a forecasting tool that will gather and analyse data on the SKUs in pilot testing; the data included macroeconomic factors, geographic factors, demographics, and other variables. Armed with this information, business leaders were able to alter pricing decisions on the fly, as needed.
Getting started
Finance teams can kick-start the digitisation process by taking an inventory of its core use cases and determining where they stand with each of the digital technologies cited here. They should ask themselves questions regarding the potential value gained from digitisation of a finance process as well as the level of feasibility of doing so. They should engage business-unit leaders in discussions about the pain points in various financial processes, undergo a systematic review of technology capabilities with IT to define system requirements and investment.
A good starting point for a roadmap/ framework is shown below, involving a 4 step approach.
Dee Singh Kothari is a senior partner in Kothari Partners
Ideas expressed and/ or methodologies in this article are solely of the authors. The author nor Kothari Partner’s accept any liability for the incorrect application of these ideas either used by companies, employees or other individuals alike.
At Kothari Partners, we have worked with various UK and overseas listed and PE/ VC backed clients across various industries to consider how their business and finance services can bring them both cost reductions and performance improvement. Our approach is to help our clients understand their current situation, identify the value and decide on the scope, vision and set of strategies for what they could achieve for their business. We help plan their implementation and support them and deliver the solution/ change needed, so it is properly and permanently embedded in their organisation. We aim to help past and future clients by delivering high-quality work to their organisation, generate real efficiencies and free up time to support better business decisions.
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