There are just six months left until Open Banking phase two begins, when customers will be able to digitally access and securely share their bank transaction data to get the most from their finances.
The initiative will encourage financial service providers to offer high quality, targeted services and in turn boost competition.
Roger Vincent, Head of Banking and Innovation at Equifax, comments: “The banking industry is set for a huge customer-centric shake-up with the implementation of Open Banking phase two in January 2018. This exciting development will dramatically change the customer banking experience, helping consumers and businesses to use their financial transaction data to access products more easily and better understand their finances.
“The initiative kicked off earlier this year with stage one, where the ‘CMA9’ (nine banks mandated by the Competition and Markets Authority) provided improved access to information such as ATM locations and product listings. The second stage is the real game changer, with bank transaction data made available digitally for consumers and businesses to share securely, and only with their agreed consent, via open application program interfaces (APIs). Through the open APIs the data can be used by authorised third parties to build new high quality and targeted services, including new digital offerings, facilitating a more competitive environment.
“The ability for transaction data to be used for automated creditworthiness and affordability assessments, fraud detection and product accessibility is endless. Customers will be able to control how their financial data is shared digitally and provide a deeper picture of the way they manage their money. This could mean a quicker, more secure and fully digital mortgage application process or faster access to finance for a new business venture. For those currently underserved by the market, for example young people or the self-employed, it could mean the start of a journey to better financial health.
“Over the next six months, banks need to embrace the move towards a more transparent banking world. To do this successfully, preparations must focus on meeting the long-term practical benefits of consumer empowered data sharing rather than approaching this change as a tick-box compliance activity.”
(Source: Equifax)
CFOs and their teams have long been dedicated to supplying and analysing the data their companies need to make solid, fact-based decisions. However, finance departments have historically been constrained by basic forecasting techniques. Here Jean-Cyril Schütterlé, VP Product & Data science at Sidetrade, explains to Finance Monthly that CFO decision making, spending and innovating is more of an art that we’re led to believe.
The underlying data collection process is often time consuming and error-prone, and the result frequently lacks depth, scope and quality. Not only is the underlying data unsatisfactory, but its processing is suboptimal. All of these approximate figures end up being copied from spreadsheet to spreadsheet and undergo many manual transformations.
This approach has many shortcomings:
- Regardless of the quality of the forecasting process, if the data is not detailed, sufficient, relevant and up-to-date, the result will be inadequate.
- Making the assumption that “all other things will remain equal” is an over-simplification. No lessons are learned from previous errors
Digitisation now gives access to more granular and diverse data about present conditions or past situations and their outcomes. Any data set that may help describe, explain, predict or even determine a company’s positioning can now be stored, updated and processed.
This 360° view provides an opportunity to discover correlations between the collected data and the figures tracked by finance executives in their modelling activity. But this trend line methodology is insufficient in itself to derive valuable knowledge from data diversity.
For the process of discovery to take place, this newly-found data trove needs to be mined with Machine Learning technology.
To put it simply, Machine Learning is the automated search for correlations or patterns within vast amounts of data. Once a statistically significant correlation is identified with a high degree of certainty, it may be applied to new data to predict an outcome.
Let’s take a simple example. Assume you are the CFO of a company selling goods to other businesses and you want to anticipate customer payment behaviour to prevent delays and accelerate total inbound cash flow.
The traditional approach would be to look at past transactions and payment experiences with every significant customer and infer a probable payment date for each.
But if you look closer at your data, you may find that your customer payment behaviours are not consistent across time, that your historical view is missing essential explanatory information about the customer’s behaviour that may or may not be specific to their relationship with your company. You end up shooting in the dark.
Wouldn’t your cash-in forecasts be much better if you had also correlated the actual time your customers took to pay you in the past, with detailed information about those transactions?
In theory, you cannot be sure that this model will perform well until you have run a Machine Learning algorithm on your own data, looking for predictive rules that relate each payment behaviour to the detailed information of the corresponding transaction or you have tested the predictive power of those rules on a set of examples.
In fact, the forecast is likely to be much more accurate than with the traditional methodology, provided that the data you fed the algorithm with were representative of your entire customer base.
That leads us to another question: can I find all this information about my past transactions while making sure they are representative?
Unfortunately, most of this information may not be readily available internally, either because you’ve never collected it or it is not flowing through your existing Order-to-Cash process. For instance, it is unlikely you know whether your customers pay their other suppliers late or not.
But SaaS platforms can capture most of this information for you and Machine Learning software will then be able to discover the predictive rules and apply them to your own invoices to forecast their likely payment dates.
But this is just a start. If inbound cash flows can be accurately deduced, so can other key metrics, such as revenue, provided the data is available. CFOs are the ultimate source of truth in an organisation. They manage skilled resources who translate facts into numbers and confer them credibility. They are therefore the best equipped to tap from as many diverse data sources as available, leveraging the power of Data Science to accurately forecast what comes next and thus gain marketing insight and competitive advantage for their company.
Thus, with their augmented capabilities, CFOs are now poised to be the digital pilots of today’s new data-driven organisations.
What is the disruption gap? How does technology and communication affect your end of year figures? How can you oversee all processes without a digital transformation? This week, Finance Monthly heard from Matt Fisher, VP of Marketing at Snow Software, who gives us all the answers and then some.
With digital transformation becoming ever more crucial to business success, the way organisations procure IT is changing. “In 2016, just 17% of IT spending is controlled outside of the IT organization. That represents a significant decline from 38% in 2012. By 2020, Gartner predicts that large enterprises with a strong digital business focus or aspiration will see business unit IT increase to 50% of enterprise IT spending.” [1] Technology budgets are moving away from a central technology department towards being the responsibility of the business unit using it. From HR procuring its own payroll software to business development choosing the best sales programme, a visibility gap is forming between what exists in the technology estate and what CIOs can measure. This gap is called the disruption gap.
However, the CIO is not the only C-suite member the disruption gap will affect. If not handled correctly, it could prove troublesome for the CFO too. The reasons for this are three-fold:
Digital transformation
Digital transformation is now key for any CFO tasked with ensuring their business is future proof. It is defined as the application of digital technologies to fundamentally change and update all aspects of business and society. The benefits of digital transformation include lower costs and improved accountability with the replacement of physical or analogue processes and interaction with digital equivalents to save time. By empowering business units to identify their own digital needs, organisations will be able to maintain agility and competitive advantage. Crucially for CFOs, this also means the ability to make one thing: profit.
Losing financial control
While the role of the CIO is changing with digital transformation, a key role of the CFO remains the same: to guard against over-spend. However, with IT budgets moving towards individual teams, a gap is forming between the knowledge of how much a budget is and what it is being spent on. Gartner [2] estimates that “by 2019, annual spending on enterprise software licenses will decrease by 30% as a result of software license optimization.”. This is with IT controlling 83 per cent of the spend. Imagine what it will be like when 50 per cent of IT spend rests not with a handful of budget holders, but potentially hundreds.
Lack of visibility
With software spend disseminated throughout an organisation, it will become increasingly difficult for IT teams to establish a clear view over what software is deployed where and how many licenses are needed compared to those held.
This loss of visibility will, in turn, increase the likelihood of unexpected and unbudgeted costs hitting the financial team, either through unplanned technology acquisitions or financial penalties issued by software and infrastructure providers for over-use of applications and cloud resources.
On the flip side, by empowering IT teams to achieve 100% visibility of all IT consumption across all platforms, the finance and IT teams can collaborate to identify significant cost and efficiency savings which can have a tangible impact on the organisation’s bottom line.
Either way, it’s in the CFO’s best interest to find a way to manage the disruption gap now and avoid unnecessary costs later.
Take action today
Bridging the disruption gap has to be a high priority for IT and finance leaders. As leading industry analyst firm Gartner[1] advises: “the focus of the software asset management discipline needs to shift from compliance to cost containment, as reduced customer bargaining power produces escalating prices at SaaS contract renewal.”
To achieve this visibility, IT teams need specialist solutions that provide full visibility of software and hardware assets (both on the network and in the cloud, physical and virtual) and how they are being used. Traditional IT Asset Management and Systems Management tools will not suffice. These teams need access to the latest breed of inventory and optimization technologies designed for organizations heavily invested in Digital Transformation.
[1] Gartner, Metrics and Planning Assumptions Required to Drive Business Unit IT Strategies. Published: 21 April 2016, Kurt Potter, Stewart Buchanan
[2] Gartner, Cut Software Spending Safely With SAM. Published: 16 March 2016 ID: G00301780
Analyst(s): Hank Marquis, Gary Spivak, Victoria Barber
[3] Gartner, Software Asset Management Reaches a Tipping Point: SaaS Cost Management Eclipses License Compliance, 06 January 2017 ID: G00315121, Stephen White | Victoria Barber
Peter Kirk, Managing Director, Accenture Financial Services
Banks must concentrate on harnessing a range of digital technologies or risk losing customers to a new wave of competitors, according to a new report by the BBA and Accenture.
Called Digital Disruption, the report features interviews with leading experts from banks setting out their vision of the future and raises questions for regulators about when a non-banking firm should be overseen in a similar way to a bank.
It urges regulators to ensure that all organisations offering banking services are regulated in the same way to give consumers certainty that they are properly protected wherever they choose to bank. Policymakers must also be minded to the threat posed to financial stability from providers of banking services not covered by existing regulation. The report argues that regulators have to take care not to hamper either innovation or competition.
Peter Kirk, a Managing Director in Accenture's Financial Services operating group and co-author of the report, said: “The traditional model of banking is under threat from new competitors and is subject to increasing amounts of regulation. Paradoxically, the digital revolution is opening up new opportunities for the banking industry. Digital is enabling banks to provide much better, more personalised services to customers and at the same time cut costs dramatically.
“The UK banking sector has made some good progress in grasping the opportunity that digital has created, but the pace of change will only accelerate and amplify the disruption.”
The new report sets out six recommendations:
Anticipation: Innovative entrants are developing lucrative products and services. In order to remain relevant, banks must look ahead to spot those parts of their business models more vulnerable to these entrants.
Speed: In order to meet rising customer expectations, banks need to increase the speed of innovation.
Branch evolution: The role of the branch needs to evolve towards offering seamless customer services.
Harness data: Exploring customer appetite for banks to use the data available to them could open a new channel of communications between banks and their customers.
Invest: In the digital age, customers expect transactions on their current accounts to be virtually instantaneous. So banks need to continue investing in their IT platforms to meet customer expectations and avoid IT outages.
Culture: Change the internal culture to one which is more agile and innovative.
