The Cloud and Digital Finance

The COVID-19 pandemic has turned everything upside down and pushed companies towards more and more remote operations. Geopolitical uncertainties and cyber risks have finally convinced the last holdouts. 

Although the adoption of the cloud is becoming more widespread, too many players still see it as additional bandwidth, a set of shared public data centers, or a simple cost-cutting lever. However, it has become an essential part of any digital strategy. Without the Cloud, because of the heterogeneous, siloed, and multiple nature of data, its exploitation would be unlimited. The Cloud offers a unique platform for hosting applications and data, computing power unmatched by traditional architectures, and unprecedented performance, opening the way to new uses.

• It greatly facilitates access to and consolidation of data, enabling greater efficiency, cost reduction, technology modernisation, and transformation of working practices.
• It frees up access to data for robots that can centralise information and create multi-format/multi-domain analyses at near-instantaneous speed.

It enables new algorithms and tools for Cloud Analytics, which can offer unparalleled power for relevant, informed, and instantaneous decision-making. Cloud Analytics (or Cloud Computing) frees businesses from the siloed nature of on-premise applications. It is at the heart of useful and effective decisions and thus contributes to performance by generating gains.

The Cloud is not only a scalable, cheaper, and more efficient IT structure, it has become the main catalyst for business transformation. All of these technologies pre-existed in decentralised IT architectures such as servers or workstations, but cloud migration facilitates their implementation in a single environment where computing capacities are increased tenfold.

So in concrete terms, what are the advantages that finance departments will gain from the operation? What are the impacts on processes, organisations, and staff skills?

Depending on the type of Cloud chosen (public, private, hybrid, multi, serverless), organisations will be able to exploit the advantages offered by the latest RPA, AI, and ML technologies, and thus positively impact financial processes by generating added value. A few examples from pioneers in high-volume BtoC companies: telecoms, banks, energy, and utilities:

• Procure to pay:
  • Algorithms are based on historical data to identify potential savings areas with suppliers.
  • Chatbots can easily and quickly respond to various queries.
• Order to cash:
  • Machine learning (ML) understands customers' payment habits, anticipates invoice payment dates, and can generate targeted dunning policies.
  • Bots automatically identify email information, recognise ordered products and transfer the information for processing.
• Treasury: 
  • Recurring and repetitive activities (payments, reconciliations, lettering...) are performed by the RPA (Robotic Process Automation).
  • Algorithms predict the factors impacting future cash flows.
• Accounting, expense reports, closing:
  • AI (Artificial Intelligence) and robots automate time-consuming and repetitive tasks (expense report entry, anomaly detection, invoice scoring, and payment lettering...).
  • Machine Learning tools help predict the number of daily entries (debits/credits) and identify anomalies.
• Shared service centres:
  • Robotic cash-pooling enables faster and more efficient reconciliation.
  • RPA instantly adjusts capacity up or down to meet demand or opportunities.
• Internal control:
  • Robots automate certain controls, e.g. reconciliations, threshold or limit checks by implementing review and validation workflows.
  • Real-time processing capability provides access to monitoring of current transactions with instant alert/control loops.
  • Machine Learning develops dashboards with an audit trail of actions taken

Few companies are committed to harnessing the power of data at a strategic level. Executives who see data as an asset in their own right are using it as a lever for growth while redefining new priorities. New analytics tools, capable of leveraging all data via data hubs and data marts, for example, can easily extract, consolidate, format on demand, and scale this information on an “à la carte” basis. Once configured and made available to data analysts, data marts process complex queries and extract information making it available to analytics tools.

In order to best serve the needs of data exploitation, the data hub must offer data marts that are:

• Reliable: the user can rely on this data
• Accessible and up-to-date: the user can access recent data
• Comprehensive: the user processes and combines data to extrapolate qualitative information
• Understandable: the user simply navigates through the data marts, understands the data and its meaning

This enables a shift from a reactive to an anticipatory mode and makes data a competitive lever. 

In addition, security is always a concern because of the sensitive nature of the information (customer, governance, legal). Good security management is proving to be a competitive advantage.

Good data hub governance should:

• Ensure data integrity (input and output) and maintain, improve, and enrich the data hub IT architecture 
• Assist operational teams in their need for new data for use in tools or reports
• Formalise and apply security standards (access, encryption, verification, etc.)

For example, the early adopters of the data hub include companies with heterogeneous or poorly integrated information systems that have to reconstruct coherent data universes from juxtaposed transactional tools.

All these transformations will change the nature of activities and tasks of finance teams. Procedures and job descriptions must also be adapted and reviewed. Once trained in these new technologies (robots, etc.), the teams can allocate more time to data analysis. Employees can evolve to become real partners in the strategy (see illustration below).

Finally, the use of new digital finance tools enables operational and accounting transaction operators to refocus on higher value-added activities (management control support, internal control, data enrichment, etc.). It also allows organisations to reduce their dependence on outsourced and offshored activities. 

The technological illusion should not, however, blind us. A successful move to the Cloud takes the form of a transformation of the finance sector that is less trivial than it might at first appear.

"Augmenting” an accounting function that has become an operator of robots or transforming a management controller into a data analyst capable of designing a data mart to answer a question from management, or handling statistical models to automate forecasts, will be neither immediate nor easy. 

New CFO jobs will emerge with their own profiles, know-how, and specific training. At the crossroads of data science, IT, and project management, they will have to keep a strong economic awareness and handle the art of sharing it with the rest of the organisation. Ultimately, the cloud is accelerating the transformation of the sector and helping to make it more attractive.