How Finance Can Benefit From Data Analytics

April 4, 2019

MONTHLY ANALYSIS

In today’s fast moving data ecosystems, getting the right insights in time can be as hard as filling a cup from a fire hydrant. The only intelligent way to achieve bottom-line results from data is to be equipped with data analytics capabilities. Data analytics tools have been around for decades and they have evolved from simple Excel spreadsheet analysis of the 1990s to advanced predictive analytics solutions post-2010s. Analytics algorithms & tools are still evolving, and the new wave of development & acceptance of new technologies such as artificial intelligence and machine learning are making the market more promising.

According to the International Data Corporation (IDC), global spending on big data and business analytics will grow from nearly $122 billion in 2015 to $187 billion in 2019. Dan Vesset – Group Vice President, Analytics & Information Management at IDC, said, Organizations able to take advantage of the new generation of business analytics solutions can leverage digital transformation to adapt to disruptive changes and to create competitive differentiation in their markets.

The banking and manufacturing industries are the top two contributors to the growth of the data analytics industry. Pharmaceuticals, insurance, energy, agriculture, manufacturing, and banking are few industries that rank highest on analytics maturity. Amongst its peers, banking enjoys a long history of leveraging data and also leads in terms of sophistication of usage. This data story is adding new tools and areas of impact in the banking industry, such as its finance function.

Industry-agnostic view of data, analytics, and related technology trends

  • Transition from manual to automated data management: Use of AI and ML capabilities are making data management categories capable of self-configuring and self-tuning. According to Gartner, by the end of 2022, manual tasks in data management will be cut by 45%; ML and automated service-level management will be the driver of this change.

  • Rising demand for high-frequency insights: High-frequency insights or continuous intelligence is emerging as an opportunity area that can help business operations with real-time analytics on current and historical data and provide actionable insights.

  • Graph processing is one of the fastest growing categories: Graph processing & graph database management is one of the fastest-growing categories. This can support organizations to explore the relationship between functions of interest such as transactions, processes, and staff.

  • Emergence of data exchanges: Data exchanges are facilitating standardized data management frameworks for seamless access and sharing the data.

  • **Commercial solutions are preferred over open source: **Commercial AI/ML-enabled solutions are likely to be preferred tools when compared to open-source platforms. Sanctity and credibility of these tools will continue to hold high importance for important business functions such as finance and accounting.

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Prescriptive and predictive analytics can deliver high impact

Banking has historically enjoyed a massive amount of customer data at its disposal and analytics has been helping the banks become intelligent to manage a series of challenges they face in day-to-day business. While basic reporting and descriptive analytics tools have now become an integral part of banks, developments in predictive and prescriptive analytics can deliver significant business impact driven by better business insights.

Predictive analytics can assist in providing foresight by identifying patterns in historical data and can suggest what will happen along with when and why. In finance and accounts, it can help in predicting accounts receivable balance and collection periods for each customer as well as to develop models with indicators that prevent control failures.

Prescriptive analytics uses optimization techniques and machine learning to assist in strategic decision-making and achieve desired outcomes.

Reporting and descriptive analytics tools are more mature but limited in their potential to deliver impact while predictive and prescriptive analytics are in early stages of adoption and indicate more sophistication & promise to deliver higher business impact.

Current state of data analytics in banking

Banks have a strong foundation to use data analytics, and advanced analytics is making it possible for banks to think about hyper-personalization. Beyond consumer data, advanced analytics can help to not only manage the increasing compliance requirements but also the costs associated with those requirements. It can also help in reducing the manual effort of analysis & reporting in finance and accounting functions.

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Data analytics can be leveraged by banks to achieve the following business objectives (illustrative):

  • Improved customer targeting, alternative marketing, distribution channels, and customer experience

  • Productivity efficiencies and improved decision-making

  • Refined risk assessments

  • Investment decision support

  • Exploring business opportunities

  • Digital transformation of banking functions, especially for new age digital-banks whose business models hinge on keeping headcount & real estate usage at a minimum

In recent years, a number of factors including the rapid adoption of FinTech, the increasing size of unmet demand, undifferentiated products in banking, intense competition as well as eroding customers’ trust in banks have brought ‘customer-focused business’ to the center stage. As a result, this is where banks are using advanced analytics the most.

To quote from a McKinsey report, More than 90% of the top 50 banks around the world are using advanced analytics. Most are having one-off successes but can’t scale up. Such banks invest in talent through graduate programs. They partner with firms that specialize in analytics and have committed themselves to make strategic investments to bolster their analytics capabilities.

What drives analytics in the banking industry? Consider this: increasing compliance requirements include an evolving regulatory environment that brings growing complexity. Each change increases the cost of compliance and poses a threat of serious fines and loss to reputation in case of non-compliance. Furthermore, the pressure of ensuring profitability remains a key concern, especially in the situation of increasing volatility in asset classes. Add to that the concern of traditional banking products losing market share to new competitors & increasing instances of transaction frauds and it’s clear why banks are focusing on the integration of risk management at the enterprise level. These are among the prime drivers of analytics in banking today.

For reasons that are similar, the need for incorporating analytics is being felt across industries, especially by finance departments in organizations.

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Evolving role of the finance function

The finance department usually serves as the back of the wagon in organizations. Conventionally, it has been a back-office, operational, and tactical function. It handles statutory reporting, management reporting, and closing the books every month/quarter/year. It also handles budget compliance, treasury, tax, cash, and capital. These responsibilities are not going away; however, things are changing, and now ambitious CFOs are exploring opportunities to do more than just recording & reporting what the organization has done. It is possible if manual efforts are reduced and teams can focus on real-value drivers – drivers of revenue/profitability. Interestingly, finance departments across industries are reengineering their process based on analytics-driven knowledge of what works for them and what not. It includes investments into dashboards and scoreboards that record performance and help in continuous planning, which translates into improved forecasts and profitability analysis for its customers and products. With advanced analytics, ML, and AI, CFOs can enable value creation, strategic engagements, and efficiency improvements in finance.

Application areas for data analytics (in finance & accounting) beyond spreadsheet automation

Finance’s contribution to key corporate initiatives includes cost-control initiatives, acquisitions, regulatory compliance, enterprise risk management, implementation of new IT systems, expansion into new markets, and other usual in-house finance responsibilities. Finance departments also collaborate with other business functions such as marketing, sales & distribution, IT, HR, tax, and treasury. If we look at key areas within finance where analytics can help, it would include:

Reporting, financial statements, and compliance: Use of advanced analytics can benefit finance department in reporting and compliance monitoring through real-time monitoring for adverse events & suspicious activity identification, data analysis for due diligence, employee behavior monitoring in finance & accounting department, and anti-money laundering (AML) measures. AML measures can include authorization diagnostics, financial intelligence, and fraud detection. Data analytics can also improve credit risk reporting and quarterly reporting for regulators. In financial reporting, it can facilitate speed and accuracy in income & expense analysis by time, region, product-line; trend analysis, and variance analysis including sources of variation

Payroll: The payroll function can leverage data analytics in form of payroll fraud forensics, eliminating payroll errors in global payroll management, staffing capacity, and performance such as overtime vs. headcount analysis, tax & compliance implications, staff redeployment based on profit analysis, and forecasting.

Financial controls: Analytics can provide better insight and increased support for finance and accounting-related processes by identifying trends and anomalies. It can also help in improving accounting data integrity and quality through more accurate & efficient reporting for increased confidence. It can also augment business & risk insights as well as enhanced efficiency & quality of processes and controls.

Accounts payable and receivables: Data analytics can be used to gain visibility into payment patterns and payment history. It can include point-in-time balance sheet analysis on accounts receivable/payable or inventory to better understand the composition of significant accounts.

**Audits: **Audit analytics can help in audit remediation, financial reconciliation, accounting compliance remediation, product remediation, customer remediation, and audit support.

Other: Tax & compliance, budgeting, corporate development & strategy including M&A & financing activities, management reports using dashboards, and visualization are other important areas where analytics can help banks.

Prevailing challenges in financial reporting

  • Financial reporting remains complicated, despite ongoing projects on simplification and convergence.

  • Near real-time availability and ability to making changes to month-end report in minutes.

  • Size and diversity (geographic) of operating units increases complexity.

  • Increasing regulatory oversight.

  • Suitable qualification of finance staff is a challenge, especially in small-scale entities.

  • Increasing push to become value managers and business partners for organizations while continuing to manage processes such as tax, management reporting, audit, transactions, and others.

  • In organizations where financial data is stored in enterprise data analytics warehouses, reporting becomes the duty of the IT department and not finance, which poses risks in terms of time-sensitivity and accuracy of reporting requests.

How data analytics can help finance

The manual approach of creating and reviewing financial reports is inefficient in terms of headcount requirement, time, as well as money. These processes can be improved by standardizing data and formulas used in the presentation of financial information. Data analytics in its best form can deliver the following:

  • Perform variance analysis on profit & loss and balance sheet analytics for actual and planned numbers to spot internal control issues.

  • Non-financial data analysis and forecast to ensure that financial results are accurate.

  • Peer group metrics and benchmarking analysis to indicate inaccurate or fraudulent financial reporting if metrics are out of line. E.g., the SEC uses SIC code to compare financial results as part of its reviews, and auditors look at peers and the overall industry to compare the company’s financial results.

  • Facilitate continuous monitoring to examine the organization’s transactions & data to assess control effectiveness and identify risks on an ongoing basis, thus reducing the cost of errors, omissions, and other deficiencies.

  • Enable effective usage of information, including investors, and regulators.

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Examples of data analytics in finance and related functions

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Apart from well-established analytics solution providers, banks are also increasingly interested in innovative analytics startups to add new capabilities in their arsenal. In 2018, YES Bank incubated Pingal Technologies that uses machine learning to create dashboards suitable for management reporting. Also, National Bank of Canada’s investment in Mindbridge Ai (audit & financial analytics solutions provider) and S&P Global’s financial analytics subsidiary Kensho’s client list (includes Citi, BoFA, and JPMorgan) are some examples that suggest that we should expect more analytics partnership initiatives by major banks which can drive changes in their finance functions.

Need for data analytics standards

Financial reporting standards is a rabbit-hole, but one can look at two main ways to standardize reports – GAAP (Generally Accepted Accounting Principles) and IFRS (International Financial Reporting Standards). The requirements such as Sarbanes-Oxley, IFRS, and XBRL raise the standards, as well as the price for failure to comply. Other key reporting standards include COREP (common reporting), FINREP (financial reporting), AMM (additional monitoring metrics), LCR (Liquidity Coverage Ratio), NSFR (Net Stable Funding Ratio), etc. The GDPR (The General Data Protection Regulation) was launched in 2018, to protect consumer data including investors, clients, and partners. The regulatory landscape for banking is dynamic, which makes changes in regulatory reporting standards ‘business-as-usual’ and sometimes, it benefits banks. E.g., In March 2019, Australia’s regulatory authority APRA announced that it would be replacing its 20-year-old direct-to-APRA data reporting system after being criticized for manual processes that were prone to potential errors and duplication. Such changes allow banks to work in partnership with third-party solution providers to manage regulatory reporting.

While there is clarity about regulatory reporting standards, the debate has already started around consumer data protection. But to realize true benefits on offer and enable increased adoption across industries and functions, the market requires wider debate and attention on data analytics standards, especially the big data analytics. The current lack of international consensus on standards is a barrier to the accessibility of data as the data owners and custodians are always hesitant to share data with other parties. It further translates into a lack of clarity about data analytics practices.

The Centre for International Governance Innovation’s (Canada) paper (Jan. 2019) suggests that a comprehensive standards framework for data analytics should include the following:

  • Foundational standards that lay general rules applicable to all sectors, such as how data is classified. This should be in-sync with regulations and requirements for firms in a sector.

  • Standards that define criteria for establishing the trustworthiness and integrity of data and data sources and other aspects of the big data life cycle.

  • Standards and specifications to deploy platforms, products, and applications that would reflect requirements set in foundational standards.

  • Ethical codes of conduct and transparency programs that involve public facing and outline accountability requirements.

  • Conformity assessment/accreditation and other measurement programs to demonstrate compliance with foundational standards.

To sum it all up, banking is bracing itself not only for continuously increasing regulatory reporting and data management guidelines but also to leverage data analytics capabilities in its back-office functions such as finance and help it evolve from a ‘reporting’ to a ‘strategic partnership’ role. Enterprising CFOs will drive the change and enable finance function to invest in tools and required talent to get the best possible out of these new opportunities.

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