Automated data processing systems enable CFOs to reshape and revitalize their finance departments, transcending them beyond accounting and bookkeeping toward big data analytics, process innovations and strategy development functions. Today's CFOs are the architects and custodians of financial ecosystems. With oracle-like stature, CFOs are tasked with providing precision forecasts, data-driven insights and tactical strategy enhancements in addition to budgeting, reporting and compliance functions to maintain the financial health of the organization.

The Automated Data Processing Sequence

Automating repeatable processes are the gateway function of data automation software (DAS). The automation cycle starts with data collection from sources including POS transactions, batch reports, spreadsheets, Internet of Things (IoT) smart sensors, time sheets and invoices utilizing optical character recognition (OCR) enabled DAS. The objective is to digitize, format and assimilate raw data for frictionless irrigation.

DAS can automate the end-to-end "legwork" of accounts reconciliation, transaction matching, journal postings and reporting. This expands the work capacity for finance teams as roles are upgraded to handle more complex tasks including oversight, investigations, compliance and analysis. Robotic process automation (RPA) software enhances and refines the automation process by enabling users to laterally calibrate more adaptive parameters/rules to further minimize the need for manual engagements. The objective here is to ensure a real-time robust data pipeline with on-the-fly accessibility.

The Era of Big Data

A seamless automated big data pipeline brandishes the CFO with the ultimate perspective and insight into the financial performance of the organization. Harvesting this abundant source of intelligence and its application to market forces requires automated big data analytics. This is where cognitive learning technologies like artificial intelligence (AI) can be levered to arm CFOs with predictive data-driven insights.

Machine and Deep Learning Engines

Machine learning (ML) and deep learning (DL) algorithms are engines that autonomously process data to generate models that "learn" to adapt as more data is piped through. Models are relentlessly back/forward/stress-tested through numerous simulations and reconstructed with better models on top of better models to reach optimum efficiency and output.

DL integrates a hierarchical identification process requiring minimal human input. It tackles a problem as a "whole" to reach an end-to-end solution. It's more costly, requiring higher-end computers that can take up to two weeks to thoroughly learn a sequence, but the results are more thorough and intuitive.

ML breaks down the problem into pieces and solves each piece individually to derive a solution for the "whole." ML requires heavy manual human input to identify parameters and rules that will generate models. ML is more economical and can process a sequence in minutes, after all human inputs are properly assigned. Comparing DL to ML is akin to driving an automatic transmission versus a manual stick-shift.

The Road to Artificial Intelligence Technology

Artificial intelligence (AI) technology implements DL/ML algorithms to fuse quantitative with qualitative data to spot correlations and patterns invisible to the naked eye. The rapid pace of AI evolution has enabled this technology to process structured and unstructured data in all formats including text, images, audio and video. AI-enabled systems are being used to virtually automate whole claims processing departments, devise dynamic risk and pricing models, predict weather patterns, map out human behavioral patterns, as American Banker notes, make investment decisions and uncover and optimize new efficiencies.

Narrow and General AI

To date, all AI is considered narrow/weak because each engine can only focus on the related task within its breadth like claims processing, forecasting trends, predicting the weather, playing games or automating repeatable processes. Narrow AI can spot correlation and patterns that would be virtually impossible for humans to find. However, humans need to take that information and apply it.

The next course of evolution is general/strong AI where systems can apply human creativity and intuition to invent and create new processes and products that have no precedents like iPod, smart watches, tablets. Strong AI can shift through unrelated fields and perform infinite tasks better with usage like humans, which is why it's also referred to a human AI.

DAS and AI Benefits

Automated data processing software is a game changer that bestows the gift of agility upon finance leaders and their finance departments. AI enables finance leaders to better communicate with investors and navigate the organization through uncertain financial climates and execute audibles from inside the pocket. DAS repositions finance departments from a reactive to a proactive stance. Finance leaders should determine which part of the automation sequence needs investment. Automation can revitalize innovation and bolster efficiencies throughout the whole organization as it manifests into a positive contagion.

Stay up-to-date on the latest workforce trends and insights for Finance leaders: subscribe to our monthly e-newsletter.

Tags: Innovation big data