Intelligent workflows 101: How to solve your finance data management problems

By and Rajesh Chopra | 4 minute read | April 22, 2019

Faced with intense competition and falling profit margins, companies are continually looking to streamline their cost of back office operations. Over the years, organizations have been able to optimize their finance cost structure by consolidating functions in shared services centers, or by migrating to more cost-effective off-shore operating models while leveraging automation to digitize transaction data.

Finance organizations are now facing a new challenge: managing high volumes of data and integrating those various data sources to drive business insights.

By creating intelligent workflows, CFOs are able to harmonize emerging technologies in the back, middle, and front end of the office and curate their business’ data.

Curating your business’ data for greater insights

Cognitive and AI-enabled technologies allow companies to manage their data challenges while augmenting and enhancing human expertise. These technologies drive new problem-solving techniques which improve productivity and open up new ideas.

Cognitive computing and AI are enabling finance departments to become strategic advisors, turning CFOs into reinventors.

In IBM’s recent global c-suite study, 29 percent of the 2,102 CFOs surveyed fell into the “reinventor” category, an elite group. Additionally, 79 percent of the reinventors in our survey enjoyed high revenue growth, and 70 percent of them grew revenue from their existing businesses while identifying new organic growth opportunities.

Our studies show that finance departments that outperform the rest understand the importance of properly leveraging both financial and non-financial data sources to drive valuable business insights. For instance, a national retail company is using local weather data to better forecast demand in weather-related products and prevent stock-outs. In addition, a global logistics company is improving its operating costs by monitoring external data sources such as geo-political information, oil pricing and trade shifts.

Many organizations struggle with poorly integrated data. Business stakeholders either cannot find the right data or cannot trust the data they find. They also face industry regulations that hinder self-service and data democratization.

Many companies try to fix their data through labor-intensive tasks ranging from writing custom programs to global replace functions. This creates productivity issues in finance shared services centers.

In addition, to address data management challenges, cognitive business process services can also improve financial operation efficiency and effectiveness by applying emerging technologies. These include:

  • Automation: Technologies such as cognitive robotic process automation (C-RPA) use machine learning and automation capabilities to speed up time-consuming tasks such as payroll and invoice processing. In the c-suite study, IBM found that 25 percent of reinventor CFOs were likely to invest in RPA technology to further their strategic goals, compared to just 15 percent of other CFOs.
  • Blockchain: Decentralized ledgers create an immutable record of transactions across many internal departments or external partners. Leveraging blockchain in the accounts payable workflow, for instance, addresses the root cause of high invoice processing costs while reducing invoice exceptions and payment delays.
  • Cognitive/Artificial Intelligence (AI) Assets: Finance organizations can automate complex transactions by using cognitive/AI applications. For example, accounts payable professionals use cognitive invoice data capture and cognitive agent assist solutions to find and analyze data in real time. Assets like these are driven by a range of AI technologies including natural language processing and deep learning.
  • Cognitive Analytics: Cognitive analytics solutions enable companies to derive new insights from their data to answer tough business problems, uncover patterns and pursue breakthrough ideas. For example, IBM’s Cognitive Collections Platform uses Watson APIs and advanced analytics to optimize decision-making and enhance the customer experience in invoice-to-cash processes. Its predictive capabilities can help CFOs forecast the impact company activities will have on revenue.

Cognitive business process services in action

We are now seeing significant growth in adoption of cognitive business services within finance organizations. Although most organizations are still focused on cognitive automation, blockchain is gaining ground in finance. This emerging technology can be used to support inter-company transaction processing, deductions management, invoicing and contracts management.

Cognitive business services are also effectively addressing challenges such as integrating large volumes of financial and non-financial data, reducing complex transaction cycle time, improving the quality of information to drive business insights, and enabling collaboration across the enterprise and business ecosystem.

The following case studies help illuminate how intelligent workflows drive results.

For a pharmacy client, cognitive finance planning and analysis have improved forecasting accuracy and unlocked profits from optimal resource allocation.

For a communications client, order-to-cash cognitive services application reduced 60-day-plus past-due receivables for another client, resulting in $38 million in savings. Embedded predictive analytics, design thinking and process transformation drove accelerated collections, reduced billing cycle times and lowered bad debt reserves.

For a financial services client, when high volumes of invoices and disputes tied up more than $100 million in capital, IBM blockchain technology helped save one client time and administrative costs by providing comprehensive visibility across the entire transaction lifecycle, and allowing stakeholders to prevent or speed the resolution of disputes.

For a retail client, enhanced store forecasts by product category to account for external events. It forward-deploys products using numerical optimization techniques driven by the signals sent in the exception forecast, thereby directing attention to products that will either outperform or underperform expectations. Through the use of structured, unstructured and social data, the supply chain can operate with a new intelligence to keep costs down.

Learn how intelligent workflows can help you achieve this next level of strategic insight in your finance organization.