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Is Finance Holding Back Your Bank’s Digital Transformation?

Is Finance Holding Back Your Bank’s Digital Transformation?
How can a Digital CFO break down the silos in the Bank and support the digital agenda in transforming the customer journey?
“Finance Transformation” programmes often focus overly on process optimisation and cost control within the Finance team itself, without paying attention to the wider Bank objectives. We believe that this is not enough - let’s be honest, the Bank cannot succeed without understanding the financial impact on customers.
So how does a visionary CFO really achieve change?

Prepare your data foundation to support digital transformation

Priority one is to identify and bring together the data that is required to enable Finance to support the digital agenda.
There is no escaping the importance of this work, but it is not quick or glamorous – or cheap. We have seen ‘big bang’ approaches fail as a result, as the organisation loses interest, or hope, when it sees no immediate return on this investment of time and money.
We therefore advocate a stepped change to timelier and systematically reconciled data, ensuring compliance with regulatory requirements (e.g. BCSC239) as well as allowing intra-day reporting. This approach delivers earlier incremental value and lower delivery risk.
As an example, changing the data will require significant changes to processes that might yield a different result - which may then have to be reconciled against the original number from the old routine. This is time consuming and requires careful management of the politics (especially regarding regulatory reporting), indicating that multiple changes may well be best done over a longer period.
Monthly data flows are often baked into multiple complex data cleansing routines that are poorly understood and not properly documented, making change risky and complex. There will be scope for improved automation – for example reducing data reconciliation and proactive auditing of departmental spending – which can drive early value that ‘buys permission’ for those reconciliation discussions.
We would advocate that processes should be mapped during each phase of activity so they can be subject to increased but incremental automation in the short, medium and long term, and to audit the timeliness of available data. Quick wins generating value and visible change for stakeholders should be prioritised.
There should, of course, be an end point - the risk of an incremental approach is that this gets lost over time. We would therefore advocate a separate strategic workstream that creates a ‘North Star’ for the organisation to work towards – perhaps with participation in, or monitoring of, such initiatives as the Banks' Integrated Reporting Dictionary.
Teradata can assist through automated documenting of data lineage, as well as drawing on our extensive experience across many large Banks with assets such as data models, the design of long-term architectural blueprints and our data foundation programme. All of this, of course, aims to build on past investments that most large banks have already made in Teradata’s award-winning cloud data analytics software platform, Vantage.

Becoming more proactive through predictive analytics

With an increasingly firm data foundation, a successful Digital CFO can introduce improvements in a way that supports the wider business transformation through use of advanced analytics. The good news is that predictive analytics is well-understood within the Bank; the bad is that Finance teams rarely have the right skills immediately on hand.
The Bank may have SAS licences in Marketing and Risk, or access to open source tools like R or Python, so choosing tools isn’t the issue and you shouldn’t be distracted by this. Make implementation of analysis the only measure of value.
What are the opportunities?
  • More accurate and detailed forecasts to anticipate and react more quickly to market trends.
  • Detecting fraud – driving down expenses by analysing spend or refunds to determine whether they are legitimate.
  • Vendor management – improved understanding of payment patterns to identify how invoice payment impacts business debtors.
This is where the cool stuff like Artificial Intelligence, Machine Learning and Robotic Process Automation can come to the fore. OK – so it may only be really cool to data scientists, but it is where significant improvements and the associated value are realised.
Does it change how Finance operates? Yes – more proactive interventions to support the wider business, faster and more efficient accounting close and more consistency with reporting. Does it change the nature of the roles within the team? Yes, again – less focus on data hygiene and more on data exploitation.
Teradata has worked with many customers who have made progress on a digitally-driven Finance Transformation. This experience reinforces delivery of your value objectives – and we’d be excited to help you to deliver success.
Portrait of Michael Ingemann

Michael Ingemann

Michael Ingemann is the Practice Partner for Financial Management at Teradata International. Michael started out as an auditor in Coopers & Lybrand (now PwC) in the early ‘90s and has ever since worked in a variety of senior finance positions in different industries (Oil & Gas and Airline). Since 2002, he has been working as a senior management consultant delivering all kinds of finance transformation projects for companies in Europe and Asia, where optimization of the Finance department and enhancing their analytical capabilities, via usage of all data, have been the key elements. At Copenhagen Business School, Michael has earned an HD in management/financial accounting (HD(R)). In addition, also from Copenhagen Business School, he has also earned an Executive MBA in General Management.

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Portrait of Stewart Robbins

Stewart Robbins

Stewart is an Industry Consultant with Teradata with about 30 years’ experience in analytics and insights activities, driving commercial value from data. He has spent several years consulting, whilst previously running insight and analytics teams at Barclays Bank and E.ON. He has extensive experience in data analysis, modelling, segmentation, market research, analytical implementation, test and learn, local marketing and data warehousing and has worked with large Banks, a major car manufacturer, public sector organisations and even with a facilities management company.
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