Why AI bots will reject or approve your reimbursements claims in future

Blockchain and IoT is enabling sophisticated data collection and analysis, resulting in actionable insights that help build the competitive advantage.

Finance is today, metamorphosing into a strategically focused, value-driven function. Within organizations, finance teams are becoming important catalysts for business growth.

CFOs are investing rapidly in various technologies to maximize the potential of the finance function through streamlining and automation.

Historical and real-time big data; and predictive analytics are connecting operations, functions and teams, providing visibility and access to insights.

Blockchain and IoT is enabling sophisticated data collection and analysis, resulting in actionable insights that help build the competitive advantage.

Intelligent automation through machine learning is the new kid on the block, that makes computers think, make more informed decisions and research faster through mathematical algorithms based on accumulated data.

Artificial Intelligence is taking strides in making minimal human intervention by supervising and ensuring work meets demands.

It is enhancing self-driving software, making expense management and other business processes simpler with fewer pain points.

Artificial Intelligence also gives employees and financial managers more time to focus on core business activities.

Machine learning tools are the next logical step in computer intelligence that is liberating finance managers from redundant and low-value processes.

AI making employee reimbursements easier 

Machine learning enables systems to learn without being programmed.

For example, with electronic receipts, machine learning enhances the expense management software to incorporate the recording of expenses through email.

With paper receipts, AI automatically extracts relevant information from the image of the receipt. Optical character recognition converts printed text into machine encoded text and stores it as data.

It allows employees to report expenses quickly and centralizes all expenses on one platform. AI restructures and fully automates the expense reporting and tracking procedures.

It reduces processing costs, allows approval workflows, prevents unintentional padding of expenses and derives and analyzes expense data for use in budgeting.

The rise of Digital assistants

Voice-activated intelligent assistants, based on machine learning technology, understand the context of business processes in different areas.

These assistants create a holistic view of a specific business situation providing for example, an overview of the status.

Experts can then analyze the information and make proposals to optimally handle a particular situation.

They gain transparency into the situation instantly, equipping them with the insight needed to make the best decisions without investing time to research.

Filed an expensive claim? AI  system can it back to you

You cannot save what you can’t see! Machine learning capabilities go beyond pure analysis of existing data.

As expenses are being filed, the AI engine stores the data as historical claims under large data sets.

Based on various data sources, machine learning algorithms identify trends and patterns, predict impacts on business numbers, and determine a futuristic view of the business with intelligent projections and what-if analysis.

It gives companies better visibility and control of business spends while opening up new pathways to reducing costs.

Finance operations

Artificial Intelligence offers immense potential to increase automation and focus on exception-handling and service quality.

For example, in a receivables management process, incoming payments need to be matched with invoices.

With machine learning, matching rates are better and improve over time by learning from data and human-exception decisions.

Over time, matching rates increase because machine learning extracts information from unstructured advice and translates them into structured data which automates the clearing process.

Organization risk and compliance

Machine learning helps detect and prevent fraud by identifying suspicious expenses. AI allows the management teams to address discrepancies real time.

The AI engine prompts for clarifications on policy-violated expenses, ensuring approver mediation is minimal.

The software goes through expenses finely and generates expense reports with accurate amounts.

Employees do not have to make elaborate spreadsheets and expense managers receive uniformly formatted expense reports with absolutely no errors.

This also increases compliance rates with automated policy checks and audit trails.

The AI engine can even analyze attributes to detect patterns and anomalies, giving companies precise information and leaving them with increased process and cost efficiencies.

Embracing automation and machine learning

CFOs are now increasingly acknowledging that digital transformation of finance is an essential, urgent and an ongoing task.

Finance leaders can become technology pioneers by recognizing and embracing today’s emerging technologies as tomorrow’s core tools.

In the Oxford Economics study, How Finance Leadership Pays Off1 it is revealed that 73 percent of finance leaders believe automation is improving their functional efficiency and giving employees more bandwidth for value-added tasks.

Through machine learning, such employees are able to find innovative methods of working and increasing output and profitability.

The cornerstone of organizational efficiency is effective cost control that ultimately leads to strong profitability and creating a congenial working environment for employees.

Automated expense management in any organization is an effective link between superior financial performance and cost control.

Ultimately, efficiency translates into freeing up of resources and their investment in long-term growth.

When companies find the best tools to enhance efficiency within and outside the finance function, the whole organization benefits.

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