Data Science and Analytics Capabilities in Accounting and Finance

What is Big Data?

Not far away from its nomenclature, ‘Big data’ is just that—BIG data. This refers to the generation, storage and processing of very large volumes of data which is typically characterized by the following three features:

Key Questions Enterprises Are Asking About Big Data:

• How to store and protect big data
• How to backup and restore big data
• How to organise and catalogue the data that you have backed up
• How to keep costs low while ensuring that all the critical data is available when it is needed

The Four Types of Data Analytics

To get a better idea of big data, it’s important to understand four types of data analytics:

What is Data Science & Its Importance for F&A Professionals?

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data on various forms.

What is the importance? What role it is playing for Finance & Accounting professionals? Data Analytics is used to help businesses uncover valuable insights within their financials and process improvement opportunities which can further help increase in efficiency. As businesses increasingly try to position themselves to reap benefits from data analytics, Finance & Accounting professionals are also poised to play a leading role in managing that transition.

Digitalisation in modern business means that Finance & Accounting professionals with the skills and aptitude to identify, analyse and use vast amounts of financial data are in high demand.

• Eliminate Inefficiencies: Predictive analytics can be used to eliminate inefficiencies and waste by identifying when transactions are at risk of being fraudulent or lost, allowing company leaders to act before it happens. This approach also enables the company to identify which customers are most likely to respond favourably to special offers, allowing businesses to build stronger relationships.
• Better Corporate Governance: The Board of Directors has a challenge to ensure that the company is run in the best way possible, while also ensuring that its stakeholders are not being taken advantage of. This can be done by focusing on good corporate governance practices, which includes transparency and accountability. Create a match between strategy and resources. Companies are increasingly using the Balanced Scorecard to align their strategies with available resources.
• Improve Control Processes & Compliance Management: Effective control processes help businesses to realise their potential and improve performance while reducing risk. They should be integrated into the business strategy, governance structures and information systems to create a culture of compliance and control that is sustainable over time. In the past, the primary purpose of a financial controller was to ensure that an organization complied with all relevant legislation and accounting standards. Today, however, organizations need to be aware of a far wider range of legislation and regulation that includes social responsibility requirements such as fair-trade practices or ISO 14001 environmental management systems.
• Reduce Fraud: Using data analytics, fraud can be reduced by classifying data and transactions to allow for early detection of fraud attempts. Fraudulent activity is easier to detect when the fraudulent activity is similar in style and character to previous frauds, allowing for simple recognition of anomalies.
• Identify Financial Reporting Issues: Data analytics can also be used to identify areas of concern with financial reporting, such as the existence of duplicate accounts or changes in a company’s reported earnings that seem inconsistent with economic factors.

From Operator & Controller to Strategic Partner

The facet of CFOs in India playing the role of an operator and controller is surely changing. The role played by a CFO has ever been evolutionary. He/she was traditionally seen as someone in charge of ensuring the right accounting governance, managing working capital, controlling the costs of the organization, balancing inventory, and managing capital expenditure. He/She was seen as a controller with a focus on transaction processing and financial reporting.

In some cases, the CFOs were able to break out of the archetype and craft a role of finance as a true enabler for business decisions. However, this process of evolution has been a long journey and required significant change management at various levels.

Now, a CFO is looked upon as a strategic business partner. The challenge is to prepare the finance leaders for this new ask from the business and society. The digital revolution in India has made this ask even more competitive–not only is the CFO expected to excel as a business partner, but the role also requires the incumbent to thrive and contribute to an ever-changing digital ecosystem.

Digital Disruption & India’s ‘Digital Bharat’ Infrastructure

As we are on the verge of digital disruption, initiatives such as Aadhaar, the Unified Payments Interface (UPI), and the Jan-Dhan Yojana are classic examples of how the economy is being introduced to Digitalization.

It has been a step-by-step implementation by the government. It first established the platform and the ecosystem which paved the inspiration for subsequent steps, a classic example being the Aadhaar enabling Direct Bank Transfer (DBT). It is needless to mention that demonetisation has only accelerated the method.

If the whole frontend is becoming more digital and autonomous, it’s only natural that the functions that support this ecosystem should continue with these rapid economic and business model changes. The way Indian businesses and finance leaders are approaching the challenge, Indian finance organisations will soon be recognised as one of the most digitally advanced over time, well aligned to the government’s vision for Digital Bharat.

Fintech Disruption & Machine Learning Capabilities

Data Science in Fintech: Machine learning, artificial intelligence, predictive analytics, and data science technologies are used by Fintech firms to improve financial decision-making and offer superior solutions.

Data Analytics in Fintech: Digital platforms create algorithm-driven, automated financial planning and investment services for investors. The client data is used to provide financial advice or to automatically invest client assets in instruments and asset classes that are better suited to their needs and goals.

Fraud Detection in Fintech: Big Data and Data Mining techniques can be used where massive volumes of fraudulent online transactions happen, and data models can be created in a manner that will allow to detect or foresee fraud in the future.

Acquiring and Retaining Customers: Detailed and diverse customer profiles are created by banks and financial institutions using external and internal customer data. It can be used to provide highly personalized services. For instance, an algorithm could be constructed to predict what additional goods or services the consumer would want to buy based on their historical purchasing behaviour.

F&A Arenas Where Data Science is Already Playing a Major Role

• F&A Decision Making: Finance & Accounting professionals are now expected to add value to decision making and manage risk. Strong data analytics gives them the required tool set for informed decision making to strengthen business leadership.
• Audit Data Analytics: Auditors monitor much larger data sets instead of a sample-based model. This will result in reduction of errors and more precise recommendations.
• Tax Consulting: Tax accountants use data science to analyse complex taxation queries related to investment.
• Investment Advisory: Big data is used by investment advisors to figure out the behavioural patterns of both customers and the market. This also assists the businesses build analytical models to handpick the best investment opportunities.
• Forensic Support Program: Forensic and analytics expertise is applied to selected audits, fraud brainstorming, journal entry testing, tailored analytics, etc.

The Analytics Readiness Deficit (Harvard Business Review Survey):

A 2019 survey of U.S. executives found that most – 63% – do not believe their companies are analytics-driven and 67% say they are not comfortable accessing or using data from their tools and resources (Source: Harvard Business Review).

Therefore, organizations across the spectrum are being earnest and nimble-footed about the analytical and technical up-skilling of their workforce. There is a huge investment going into technology and training. Amazon’s USD 700m towards reskilling its employees in tech training over the next 4 years is just one example.

Lessons from Indian Public Sector Bank Computerization:

A prime example is the banking system in India. Computerization of public sector banks and their evolution from the hardbound ledgers to core-banking would have never been achieved if the employees did not adapt to the change, adopt new technology, and most importantly bring about a shift in mindset.

The leaders of tomorrow are going to be those that understand data and therefore the impact of data quicker and are ready to influence it within the most effective way possible by leveraging all the tools, techniques, and processes available in this new Digital Age.