Robotics and cognitive computing

Finance organizations are finding it hard to keep pace with the growing requirements of their businesses. Technology has enabled organizations to receive significant amount of data which need to be optimally utilized for decision making. Information is flooding into business and pushing data volumes through the roof. Apart from internally generated business data, there is a lot more outside the business which influences decision making. Big data, social media, The Internet of Things, and many more sources which cannot be ignored.

To manage the influx of such large data, organizations will need to develop a robust ecosystem which will enable utilization of this data for effective decision making. Organizations in India are significantly investing in futuristic technologies such as robotics, cognitive, blockchain, in-memory computing, visualization, etc., which will potentially come together to define the way Finance is going to function in future.

If one has to look at the evolution of Finance and particularly the evolution of technology interventions in Finance which started with a set of ERPs followed by workflow solutions, business intelligence solutions, and Finance governance solutions. All these technology solutions helped Finance organizations keep pace with the dynamic business environment of those times and provided relevant information for decision making.

The eight exponential technologies

There are eight exponential technologies that will redefine the way Finance of the future is going to be conducted. These are: Robotic Process Automation (RPA), Cognitive Computing, In-Memory Computing, Visualization, Blockchain, Internet of Things, Advanced Analytics, and Mobility.

These technologies can sit on top of a cloud environment that uses scalable, elastic technology to deliver services over the Internet. Instead of making large investments upfront, finance can get the full stack of finance functionality “as-a-service”, delivered through public, private, or hybrid clouds. These technologies will bring in a wave of disruption to core Finance areas.

The article highlights two such exponential technologies that have a wide ranging impact in shaping the future of finance profession.

Robotics Process Automation (RPA)

RPA is a computer-coded software, commonly referred to as BOT, that emulates human actions and is able to drive automation of rule-based processes. It is an ideal automation technique for any process that has heavy dependence on data entry, data manipulation, triggering responses, and communicating with other digital systems.  Organizations see this ‘IT-light’ technology as a blessing to dramatically bolster process efficiency levels, accuracy levels, and throughput for transactional processes, without having to navigate IT organization complexities required for other automation interventions.

Organizations using RPA solutions typically experience benefits beyond cost reduction such as the following:

1. Decreased cycle times and improved throughput: Software robots are designed to perform tasks faster than a person can—making 24x7 operations possible.
2. Flexibility and scalability: Once a process has been defined as a series of instructions that a software robot can execute, it can be scheduled for a particular time, and as many robots as required can be quickly deployed to perform it. Equally, robots can be quickly reassigned when more important processes arise—as each robot is typically capable of performing multiple processes.
3. Improved accuracy: Robots are programmed to follow rules and robots do not make typos.
4. Improved employee morale: The tasks and processes most suitable for automation are typically the most routine and least enjoyed ones, and employees relieved of them can focus on more rewarding and higher value activities.
5. Detailed data capture: The tasks performed by a software robot can be monitored and recorded at every step, producing valuable data and an audit trail that can support further process improvement and help with regulatory compliance.

When applied to a typical Finance environment the results help understand why there is a surge of interest in RPA in the finance organization. Size of opportunity of savings in headcount often ranges from 30 to 50%, with highest amenability experienced in Accounts Payable, Fixed Assets, and Time & Expense (T&E) processes. In addition, there are pockets of opportunity in Accounts Receivable (sales order processing, debtors reporting), and General Accounting (journal entry processing, reporting), which are also being actively considered in the RPA portfolio as a testament to the strength of the RPA solution.

Cognitive computing

Perhaps the most disruptive set of technologies upending the world of finance lies in artificial intelligence (AI) applications. A subset of AI is cognitive computing which by definition is “a self-learning system that uses data mining, pattern recognition, and natural language processing to mimic the way the human brain works.

The goal of cognitive computing is to create automated IT systems that simulate human cognitive skills, grinding through mountains of data to automate insights and reporting in real time. Cognitive solutions may be deployed from the cloud and offered as a hosted service or deployed as in-house servers depending on the organizational IT landscape and requirements.

When used in Finance, cognitive technologies working alongside the existing ERP systems and Robotics can upend operational finance and bring about unprecedented speed, agility, and transparency to the processes.

Machine learning in Internal Audit

In audit, because of recent advances in machine learning, standard audit techniques like sampling are on the verge of becoming obsolete. This directly affects the audit industry’s employment model, which is dependent on hiring scores of graduates to carry out mundane administrative work.

With a cognitive BOT in the Audit team, the auditors can analyse an entire set of accounting journals, rather than just taking a sample of journals that provided a snapshot. This wider view can highlight anomalies like entries posted by unexpected people or at odd times, such as weekends, include analysis of the entire set of expenses and potentially expose claims for personal travel, etc.

Machine learning in Financial Planning and Analysis (FP&A)

For the FP&A function, the key aspect of planning is to obtain higher level of accuracy in understanding and prediction of sales volumes. Inaccurate revenue forecast remains one of the biggest risks for CFOs. Machine learning has the potential to improve this process by:

1. Powerful trend analysis: Humans do not have the capacity to scan vast amounts of data and come up with scenarios and identify patterns. This is where algorithms are powerful. They can examine structured as well as unstructured data and come up with meaningful and impactful analysis. 
2. Forecast accuracy: Forecasts are generally driven at product level sales values. Machine learning algorithms can detect patterns at lower level feeder drivers such as brand categories, product categories, purchase orders, and even invoices to discover interesting relationships and dependencies, which can then inputted into the planning cycle, thus enabling more accurate forecasts.
3. Dynamic forecasting: Machines are capable of dynamically updating scenarios based on changing input parameters. They can simulate and re-simulate scenarios while tweaking data and thus act as informative decision tools. This makes the planning cycle more elastic.
4. Interactive self-service: Machine Learning is now allowing companies to build self service solutions on platforms that can mine swathes of data and provide relevant and contextual responses to any standard human query around financials.

Machine learning in other Finance areas

In other areas tools have been developed which use machine learning technology to scan electronic papers and automatically identify and extract key accounting information from a wide range of documents like contracts, policies, agreements, purchase orders, sales orders, commercial invoices, etc. These artificial tools then improve with every human interaction, which will over time increase their power as they gather more information.

Natural Language Generation (NLG) in Finance

NLG in Finance can be used in generating cumbersome Financial and Statutory and compliance reports which can consume significantly amount of human effort.

The future of finance will have a very important place for the cognitive BOTs. While the humans would still be around to develop the strategies, setting goals, designing the future road map, etc., the BOTs would help eliminate the cumbersome manual efforts of producing the reports. The time freed up can be effectively used by the Finance department to provide powerful analytics which will help the Management to make quick, yet deeply insightful decisions.