In an interaction with Adlin Pertishya Jebaraj, Correspondent of Finance Outlook India, Prakash Ravindran, CEO & Director at InstiFI shares how digital finance and automation are redefining accounting by replacing manual processes with real-time, data-driven systems. In his words, "Artificial Intelligence (AI) is helping in making the forecasts more accurate, giving a clear picture of liquidity and better capital allocation while at the same time errors and close cycles are being minimized".  

Prakash‍‌‍‍‌‍‌‍‍‌ Ravindran is a fintech and digital finance expert, with deep expertise in accounting automation, payments infrastructure, and technology-driven financial operations. He has broad exposure in the financial services industry and has been instrumental in the financial function transformation journey to data-driven, automated systems, which are efficient, accurate, and compliant.  

How do you see the current global shift toward digital finance, and what macroeconomic trends are accelerating automation in accounting workflows today?  

The global shift toward digital finance today reflects a fundamental transformation. Today, more firms and institutions are digitizing financial services, such as payments, reporting and banking operations, making finance faster, more accessible, and increasingly data-driven. This creates strong incentives to automate accounting workflows to match the speed, scale and complexity of modern finance. 

Macroeconomic forces driving automation in accounting include tighter margins, labour shortages and inflation, AI adoption and automation to scale. AI use across business functions rose sharply, with 88 percent of firms using it in at least one area by 2025, accelerating deployments in reconciliation, invoice processing, and reporting. As digital payments, fintech services and real-time transactions proliferate, the volume of financial data and transactions will demand automation to maintain accuracy and speed.  

Also Read: How Real-Time Data & AI Are Driving Business Valuation in SME Sector

What are the major challenges organizations face when transitioning from manual processes to automated accounting systems?  

There are a number of challenges that organizations encounter in their transition to automated accounting systems. Change management is the most considerable problem: since employees are used to spreadsheets and manual checks, they tend to be reluctant to use new tools, and this hinders rapid adoption. The other significant obstacle is data migration. The legacy data may be costly and dangerous to clean, map, and integrate into the new systems. By a survey, approximately 37 per cent of companies identified data concerns as the largest challenge when implementing AI or automation in general. 

Process redesign is also a source of tension because with automation, there must be standardization and most companies do not have this. The lack of skills also makes the transition more difficult; finance teams have to get used to using AI-enabled tools and dashboards. Also, it may be difficult and costly to integrate with existing ERP, CRM, and billing systems. 

How can automation enhance cash forecasting accuracy, liquidity visibility, and capital allocation decisions? 

Cash forecasting, liquidity visibility, and capital allocation are greatly empowered by automation by subverting manual and error-prone processes with real-time, data-driven intelligence. Forecasting models that are based on AI examine the cash flow, the behaviour of customers paying, seasonality, and market signals to generate more accurate forecasts. Automated forecasting systems have been proven to cut forecast error by 20-50 percent and provide a better cutting-edge upon which the team of finance can plan. 

The visibility of the liquidity is enhanced because automated systems combine the information of ERPs, banking systems, AR/AP modules, and billing systems to produce the current cash positions in accounts, all regions and business units. Having greater precision in their forecasts and real-time information, organizations are able to make smarter capital allocation decisions to tune the working-capital cycles or investment in time. With automation, there is also the ability to conduct fast-scenario modelling in which CFOs are able to measure the financial consequences of changes in the market or strategic action before committed capital. 

How is AI reshaping core functions such as accounts payable, receivable, reconciliations, and financial close cycles?  

AI removes human errors and introduces speed, accuracy and predictability of daily accounting processes. AI can be used in accounts payable to extract and verify invoice information, match with POs, identify discrepancies and automatically route approvals. AI is used in the accounts receivable to predict delays during payments, prioritize collections, and convert reminder processes, which enhance cash conversion cycles. 

In case of reconciliations, the intelligent matching engines can be used to quickly match bank statements, ledger records, and transaction feeds to detect exceptions with high accuracy. This reduces dramatically the amount of work to do at the end of the month. For instance, automated reconciliation can make the process up to 85 per cent faster than a manual method. During the financial close, workflows monitored by AI coordinate the work and monitor dependencies, as well as the consistency of data in the systems. The outcome is a faster and open close cycle. 

How will automation shape cross-border financial operations, multi-currency reporting, and global compliance requirements in the future?  

The automation will fundamentally apply to the management by global finance teams of cross-border operations, multi-currency reporting and compliance. Smart payment and treasury solutions will simplify the cross-border operations with real-time FX rates, offer settlements, and minimize manual reconciliation. Financial reporting automating multi-currency environments converts, consolidates and reports financials across subsidiaries, removing differences arising due to fluctuating exchange rates or disparate local practices. 

Compliance will also be more proactive: country-specific tax rules, report formats, and filing schedules will be built into automated platforms and produce audit-ready data and less regulatory risk as businesses expand on a global scale. Automated cross-border payment systems have also assisted organizations to reduce payment processing costs with an average of 30 percent, which is one way automation has reduced efficiency and suppressed costs.