With the influx of Big Data, finance operations today can easily become complex, slow and inefficient across both traditional brick-and-mortar businesses and newer, digital disruptors.

Finance executives have continuously attempted to address the challenges of cost and efficiency of the back-office with shared services and business process outsourcing, in addition to business process automation with traditional ERP tools.

However, the complexity of high volume finance operations such as order-to-cash, procure-to-pay, record-to-report, treasury and working capital, audit and compliance, expense management, payroll management, and others, have always created drag in the finance organization.

Going Beyond Just Digital

Finance transformation is actively embracing digital transactions and going paperless with digital invoices, digital payments, and electronic documents. The proliferation of advanced digital technologies — Big Data, machine learning, Artificial Intelligence (AI), robotics process automation, chatbots, digital assistants, mobile payments, blockchain, IoT, and many more — presents unprecedented opportunity to disrupt the costly, administrative, and bean-counter mindset of the back-office to give way to a modern data-driven finance organization: a connected, cognitive, cost-effective, and customer-centric finance organization.

Data-Driven Finance: The Modern Mindset Disrupting the Back-Office Functions

It is this new decision-making and automation mindset that is making a paradigm shift in the finance industry. For instance, reimagine your customer-to-cash operations driving precise digital collections treatment to each customer, based on their payment behavior and maximizing cash flow for your company. Or your digital treasury making precise working capital decisions based on cash inflows and cash outflows patterns from customers and vendors. In addition, data-driven finance empowers marketing in daily customer acquisition campaigns to focus on high-propensity-to-buy customer segments, or helping sales with predictive insights to identify low risk and high value customers for faster closing and onboarding.

The Finance Data Lake: Next-Generation Data Warehouse

A significant amount of time in finance operations is spent gathering information and administrative activities, while very little time is spent in processing and analysis. Core to data-driven finance is the new Big Data architecture of a modern data lake – a central repository of data, documents and information. Streams of data can come into the data lake in batches or real-time from ERPs, document management systems, banks, lockboxes, credit bureaus, payroll systems, vendor management systems, expense systems and much more.

Open source systems such as Hadoop can store massive amounts of data at a very low cost, while giving enormous computation power to drive insights. Data discovery enables finding all finance related information from several systems and creating a map for easy information access. Robotic process automation enables data collections from legacy systems or from external web-based systems to stream into the finance data lake.

A well-managed data lake with good controls on security, governance and data access can serve as a modern data warehouse without the hassles of a traditional data warehouse. Smaller data lakes focused on each process can be created and joined with others for larger view. Reimagine order-to-cash data lake, procure-to-pay data lake, treasury data lake with a simple google-like natural language enabled search to find all documents or data related to your customer or vendor or a sales order.

The Finance Analytics Hub: AI-Powered Crystal Ball for Finance Predictions

Big data analytics with machine learning and AI can churn petabytes of data to reveal interesting patterns happening in your organization. Descriptive, predictive and prescriptive analytics can become the beacons and daily dashboards for managing global finance operations and shared services.

For example, customer payment patterns can help you predict when and whether a customer will pay, delay or dispute an invoice, and customer churn analytics can give you which customer you will lose, when and why.

AI can read check images, match up to the right customer invoice and quickly identify problem payments. Text analytics and natural language processing can read all documents, associate them with your transactions and give out fraud and compliance related alerts.

The finance analytics hub is the concept of a modern business analytics suite based on advanced analytics that gives descriptive, prescriptive, predictive and cognitive analytics for finance executives to easily comprehend complex patterns and trends associated with finance operations. Supervised machine learning models take your historical information and predict the outcomes of your key performance metrics such as Days Sales Outstanding (DSO), Collections Efficiency Index (CEI), cash flow forecast and many others. Reimagine your day starting with daily dashboards with quick performance stats and alerts, predicting problem areas in your operations that need your attention. AI is giving way to new user interfaces such as chatbots and digital assistants.

In Conclusion

Reimagine asking your Siri-like finance assistant questions in plain English, and receiving the exact information you need in just seconds. Reimagine your finance operations automated in a new way, powered with predictive analytics from the finance analytics hub and preparing daily task lists for each of your staff around the world, to drive the business process efficiency to completely new levels.

The possibilities are endless, and the journey of finance transformation only gets more exciting as we continue to adopt more emerging technologies.

About the Author

Veena Gundavelli is Founder and CEO of Emagia Corporation. She is also a U.S. Special Representative for the Department of ITE&C, Government of Andhra Pradesh, India, and Co-Founder and Vice President, Analytics, at Solix Technologies, Inc. Veena holds a Masters in Computer Engineering from Santa Clara University, and a Bachelors in Electronics and Communications Engineering from Osmania University.