Reconciliation is a process of comparing two related sets of records or two accounts at the end of an accounting period. This is a critical accounting process for identifying accounting disparities, fraud, and other serious bookkeeping issues.
Regular reconciliations rectify issues like data redundancies, omissions, and errors in the amount other than pinpointing unauthorized transfers and withdrawals. Any discrepancies in reconciliation should be reported, and corresponding adjustments are added to balance both accounts.
The process of Reconciliation differs depending on the account type, business industries, and the volume of data that needs to reconcile. Different types of reconciliation are available in the industry, such as bank reconciliation, credit card reconciliation, positions reconciliation, balance sheet reconciliation, suspense account reconciliation, etc.
Businesses use various methods of reconciliation depending upon the volume of data to be reconciled, complexity, and frequency. Tools like spreadsheets are the most common way of handling the reconciliation along with accounting software and Robotic Process Automation.
Across the industries, time–consuming and complex reconciliation process are now handled with the help of modern technologies such as AI and machine learning.
Over time, here is how the modern reconciliation process has evolved from spreadsheet to the use of cutting-edge solutions
Spreadsheet, a past way of doing Reconciliation
Spreadsheets are the most widely used tool for Reconciliation. While these may work well for small organizations where the data volume is low, they may be unreliable in case of ever-increasing breadth and depth of data reconciliations.
Spreadsheets work well for data analysis, and recurring calculations, although it has some major disadvantages while working for an end to end reconciliation process.
- Difficult to keep the track: Document version history may not always help for auditing purposes, and key document changes may go untracked.
- Lack of Flexibility: It is difficult to handle the high volume of data in spreadsheets
- Manual Process: In this case, input data from various sources is entered manually. This is a bulky, time-consuming job and is prone to numerous errors
- Data Loss: There is a possibility of losing the data when the information is handled manually amongst the multiple users and systems
- Cannot be integrated with other systems
Partial Automation: Rules-based Solutions
There are many accounting software suites or standalone reconciliation tools available for large-scale reconciliations. Some drawbacks of spreadsheet-based reconciliation like manual process, lack of flexibility, and analytics are handled well in these Solutions.
These solutions can also be integrated well into systems like ERP that automate a large number of reconciliation processes with pre-defined rules, therefore reduce the possibility of manual errors. But here is a downside to these solutions:
- Handling multiple data sources and fields may require a large amount of work.
- Rules-based record matching may not always work with complicated calculations.
- Inability to handle Exceptions: Rules-based Solutions can handle matching efficiently, but exception management is still difficult and expensive. Many organizations are finding it strenuous to resolve breaks on time and meet compliance standards.
AI-based Reconciliation Solutions, smart way of handlining reconciliations
AI-based Reconciliation Solutions emerged as a smart way of handlining reconciliations. AI and machine learning are revolutionizing the way businesses are managing end to end reconciliation process. Modern technology solutions such as Ascent AutoRecon, a first-in-industry, readily configurable, AI-enabled solution provide clients with 99% reconciliation in real-time. It helps financial services to increase productivity and value in the reconciliation process.
The next-generation software solution AutoRecon offers:
- Highly Scalable: Effortless handling of colossal workloads – millions of transactions/hours
- Extremely Flexible: Matching Rule engine for tackling complex workflows in approvals and audits logs
- Rich Management Dashboard: Wealth of information with analytics and dispute aging analysis
- End to End Claims Management: Disputes lifecycle – from initial entry to final resolution
- Complete Automation: Real-time integration of multiple data sources and full visibility (end-state of each transaction)
- Last but not least it comes in two models: SaaS and on-premise