In at this time’s advanced monetary panorama, companies are drowning in a sea of financial institution statements. In 2022, a staggering 98.6% of People held transaction accounts, producing an unprecedented quantity of monetary information.
For companies processing 1000’s of financial institution statements every day—from insurance coverage firms to monetary establishments, financial institution assertion processing presents a problem and a good greater alternative for automation.
Conventional handbook processing and reconciling, which consumes a median of 10-12 hours per week, is now not an choice. It typically results in errors, delays, and missed insights, making assertion administration a logistical nightmare.
On this article, we’ll discover purposes of AI and automation for financial institution assertion processing. We’ll additionally information you thru organising an environment friendly financial institution assertion processing system and share greatest practices to remodel this information flood right into a strategic asset.
What’s financial institution assertion processing?
Financial institution assertion processing is extracting and analyzing monetary information on financial institution statements.
- It entails extracting key particulars from financial institution statements, reminiscent of transaction quantities, dates, descriptions, account balances, payee names, account numbers, and transaction varieties (e.g., debit or credit score, and so on.
- The financial institution extract data is then transformed right into a structured format for these and different accounting operations.
- The extracted information is then despatched for financial institution assertion evaluation, additional processing, and accounting.
Financial institution assertion processing is crucial for correct reconciliation, auditing, and monetary reporting.
Lately, AI-powered software program instruments utilizing pure language processing (NLP) and machine studying (ML) have revolutionized this course of.
These instruments allow companies to course of statements quicker and extra precisely by automating transaction information extraction, categorization, and evaluation. The result’s improved effectivity and scalability in monetary operations, decreasing human errors and saving time for higher-value monetary duties.
Steps in financial institution assertion processing
Let’s take a look at the steps concerned in financial institution assertion processing and some greatest practices for every step:
Gathering financial institution statements
Financial institution statements are available numerous codecs, reminiscent of e-mail attachments, PDFs, bodily copies, or spreadsheets, every requiring a distinct strategy.
Historically, accountants and bookkeepers manually enter information from financial institution statements into their accounting software program. Amassing and monitoring all such financial institution statements from numerous sources is tedious and wastes valuable time.
This makes sustaining an environment friendly audit path tough and hampers the month-end and year-end processes.
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Greatest practices: Use an automatic centralized platform to tug financial institution statements from any supply. If you’re utilizing paper statements, guarantee all of the digital information are correctly scanned and readable.
Knowledge extraction instruments robotically extract pertinent data from financial institution statements utilizing machine learning-enhanced optical character recognition (OCR) know-how.
These instruments can determine key particulars reminiscent of transaction dates, quantities, and descriptions, decreasing the effort and time spent on handbook information entry.
These instruments come in several codecs, like template-based information extraction instruments, and superior AI-powered OCR software program.
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The price of utilizing template-based information extraction instruments is near 17% increased than AI-based software program.
If you’re utilizing a template-based software, guarantee it’s suitable with totally different assertion codecs and templates for seamless integration.
Nonetheless, for those who obtain financial institution statements in several codecs, languages, currencies, and templates, contemplate investing in an automatic AI-based information extraction answer that depends on extremely correct superior OCR and machine studying fashions.
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Arrange rule-based workflows to determine and take away duplicate entries and carry out human evaluations for advanced or ambiguous transactions.
Reconciliation
This step entails matching the extracted financial institution assertion information with the corporate’s inside monetary information.
Handbook reconciliation is a sluggish course of that entails accountants or finance groups evaluating 1000’s of transactions throughout a number of statements and accounts. EY stories that AI reduces reconciliation errors by as a lot as 90%, reducing the chance of mismatched transactions.
AI can course of 1,000+ transactions in below 2 minutes, whereas handbook reconciliation for a similar quantity would take a number of hours and even days.
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Whereas implementing AI in financial institution assertion reconciliation, you can begin by automating the reconciliation of vendor funds earlier than increasing AI to deal with overseas foreign money transactions or intercompany reconciliations.
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Create rule-based workflows that permit AI to deal with repetitive duties however contain human intervention when AI flags a fancy exception or a high-risk anomaly.
Discrepancy identification
Discrepancy identification is a essential side of financial institution assertion reconciliation. Throughout reconciliation, any mismatches are flagged for additional evaluate.
Routine discrepancies reminiscent of timing variations (e.g., uncleared checks and financial institution charges) are frequent, and AI can simply detect and flag these discrepancies.
Automating the identification and categorization of those high-frequency discrepancies reduces the workload for human reviewers.
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This could scale back fraud-related losses by 30-40% from discrepancies reminiscent of duplicate invoices or suspiciously massive funds.
Changes
As soon as the accounting staff identifies and explains discrepancies, they make the required changes. These changes be sure that discrepancies between financial institution statements and inside information are resolved promptly and precisely.
With pre-defined guidelines, AI can deal with routine changes, reminiscent of small variances in quantities or foreign money conversions, by robotically updating the accounting system as soon as these changes are validated towards historic information.
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Arrange predefined guidelines for changes because of frequent discrepancies, reminiscent of rounding errors or clearing delays, in order that AI can robotically resolve them or flag them as anticipated with out human intervention.
Prioritize discrepancies primarily based on threat and use AI to categorise them into low, medium, and excessive precedence.
Transaction evaluation
After reconciliation, companies typically analyze the transaction information for insights.
Accountants evaluate particular person transactions to make sure they align with inside monetary information. AI can improve transaction evaluation by automating categorization, detecting patterns, and figuring out discrepancies.
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Use superior AI algorithms to deal with multi-currency transactions. Such instruments robotically apply the proper alternate charges primarily based on the transaction date and scale back the probabilities of miscalculations because of fluctuating alternate charges.
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Organising automated classification and categorization of transactions primarily based on predefined guidelines and historic information can considerably enhance the financial institution assertion evaluation.
Reporting
Lastly, the processed information is summarized into stories highlighting the group’s monetary standing. These stories can embrace insights on money stream, expenditures, and total monetary well being, offering stakeholders with a transparent understanding of the corporate’s monetary place.
Implement AI-powered reporting instruments to automate the creation of monetary stories. These instruments can pull information immediately from reconciled financial institution statements and generate stories with minimal handbook enter, making certain consistency throughout reporting intervals.
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Utilizing NLP instruments to generate simple-language explanations of monetary stories can assist construct an excellent narrative. These stories ought to spotlight key tendencies, variances, and insights to make stories extra accessible to non-financial stakeholders.
AI-powered applied sciences in financial institution assertion processing
Synthetic Intelligence (AI) has reworked financial institution assertion processing, making it quicker, extra correct, and able to dealing with massive information volumes. AI-powered programs are revolutionizing how companies categorize transactions, detect fraud, and keep monetary accuracy.
Here is a more in-depth take a look at the AI-enhanced applied sciences that play a key position in trendy financial institution assertion processing:
AI-powered Optical Character Recognition (OCR) instruments are indispensable in financial institution assertion processing, particularly when coping with unstructured codecs like PDFs, scanned paperwork, and handwritten textual content.
These instruments precisely extract important particulars reminiscent of transaction quantities, dates, descriptions, and account numbers, no matter formatting complexity (tables, logos, and so on.).
AI-driven OCR is far more superior than conventional OCR and might extract key data with as much as 99% accuracy. As an illustration, platforms like Nanonets can extract information from various codecs and languages seamlessly.
The AI engine reads and organizes information right into a structured format, serving to companies streamline reconciliation and monetary reporting. This stage of automation minimizes handbook enter, reduces errors, and improves the general pace and accuracy of monetary information administration.
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Key profit: Automating OCR processes can scale back handbook reconciliation time by as much as 90%, permitting companies to concentrate on strategic monetary choices.
Pure Language Processing (NLP)
Pure Language Processing (NLP) enhances financial institution assertion processing by deciphering and categorizing transaction descriptions, permitting for higher transaction group.
Not like easy keyword-based categorization, NLP instruments perceive the context of transactions. For instance, NLP can differentiate between “AMZN*XXXX2” (an Amazon buy) and “TRANSFER TO SAVINGS” (a financial institution switch), precisely categorizing these transactions.
In apply, this permits for extra environment friendly monetary administration and correct categorization of bills, serving to companies keep correct budgets, determine spending patterns, and optimize sources.
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Key profit: NLP instruments can scale back the handbook effort of categorizing transactions by as much as 80%, considerably enhancing the accuracy of monetary reporting.
Sample recognition and fraud detection utilizing ML
Machine Studying (ML) fashions analyze historic transaction information to detect fraud and acknowledge patterns in spending habits. ML instruments constantly study from new transaction information, enhancing their skill to flag anomalies that deviate from established patterns.
For instance, if a buyer usually makes small purchases from a well-known vendor however out of the blue has a big transaction at an unknown vendor, the system may flag it as uncommon.
ML-driven fraud detection programs supply real-time monitoring and adaptive studying, enabling companies to guard their property from unauthorized transactions and determine potential monetary dangers early on.
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Key profit: ML fraud detection programs enhance threat administration and scale back potential monetary losses by as much as 70%.
Open banking and API integrations
Environment friendly financial institution assertion processing depends closely on integrating monetary programs reminiscent of accounting software program, ERP platforms, and databases. With Open Banking and API integrations, companies can immediately join their financial institution assertion processing instruments with these programs, making certain seamless information stream.
Instruments like Nanonets combine with in style platforms like SAP, Xero, Sage, Netsuite, and QuickBooks, enabling automated information extraction and direct import of key monetary data into accounting programs. This eliminates information silos, reduces handbook information entry, and ensures consistency throughout monetary stories.
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Key profit: Companies can scale back operational prices by 30-40% by automating monetary information stream between programs by means of API integrations.
Automated reconciliation
AI enhances reconciliation by robotically evaluating information from financial institution statements with inside monetary information (e.g., common ledgers). AI algorithms match transactions primarily based on particulars like dates, quantities, and payee names, flagging discrepancies for handbook evaluate.
By automating reconciliation, companies save time and scale back the chance of human errors, making certain that their monetary statements are correct. This course of is essential for firms with excessive transaction volumes, the place handbook reconciliation is time-consuming and error-prone.
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Key profit: Automating reconciliation cuts reconciliation time by over 50%, whereas enhancing accuracy and transparency in monetary reporting.
Superior analytics and predictive insights
With real-time processing, AI-enabled instruments can ship immediate insights into monetary information. Think about having the ability to spot tendencies as they occur—this implies you’ll be able to funds extra precisely and put together for future bills with out the guesswork.
AI doesn’t simply crunch numbers; it may additionally analyze buyer habits by means of sentiment evaluation. By taking a look at transaction information and buyer interactions, AI helps uncover what prospects suppose and really feel. This perception means that you can tailor your providers to raised meet their wants.
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Key profit: AI analytics instruments improve monetary forecasting accuracy by as much as 25%, offering companies with deeper insights into their money stream and monetary well being.
Contextual studying for steady enchancment
AI programs evolve with contextual studying, adapting to the info they course of and the suggestions they obtain. For instance, if a transaction is misclassified (e.g., private expense versus enterprise expense), the consumer can appropriate it, and the system will study from this correction, enhancing future categorizations.
With extra information and constant suggestions, AI fashions change into more and more correct over time, minimizing handbook intervention. Companies implementing AI-powered options profit from programs that frequently enhance, decreasing reliance on human oversight and enhancing total effectivity.
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Key profit: Over time, contextual studying can scale back handbook changes by as much as 60%, making a extra autonomous monetary administration course of.
The right way to arrange an automatic financial institution assertion processing workflow
Right here’s a step-by-step information to organising a financial institution assertion processing workflow utilizing Nanonets, an AI-powered information extraction software:
Import all financial institution statements
- Enroll on app.nanonets.com totally free and choose Pre-built financial institution assertion extractor.
- Acquire and import all of your financial institution statements from numerous sources, reminiscent of Dropbox, Google Drive, E-mail, Zapier, and OneDrive.
You too can arrange an import block primarily based on particular triggers, such because the arrival of a brand new file in your cloud storage.
Customise information actions
- Arrange superior conditional information motion steps on your financial institution assertion processing, reminiscent of –
- Assessment the extracted information and customise your output by maintaining solely the related fields.
- Take away pointless fields or add related missed fields to make sure a clear output.
Arrange rule-based approvals
- Configure and arrange guidelines –
- Flag statements primarily based on fields like excessive transaction quantities or lacking account numbers
- Flag duplicate financial institution statements
- Organising approval course of with a number of reviewers – obligatory and for flagged
Export for processing
- You possibly can obtain or export the ultimate leads to totally different codecs, reminiscent of CSV, XML, Google Sheets, or Excel, or create a shareable hyperlink to share with the staff.
- For post-extraction processing, combine with third-party instruments utilizing built-in integrations –
- Combine with accounting and ERP software program like Quickbooks, Zoho Books, Sage, Xero, Netsuite
- Database export choices, reminiscent of PostgreSQL, MySQL, and MSSQL.
- Export to customized Python scripts for specialised processing
- Or combine with any software by way of API
Greatest practices for automated financial institution assertion processing
To make sure profitable automated financial institution assertion processing, contemplate the next greatest practices:
Integration with present monetary programs
Create a digital ecosystem by integrating your automated financial institution assertion processing instruments with present monetary programs.
This integration ought to transcend easy information switch; goal for clever interactions the place processed assertion information robotically triggers related actions in your accounting software program, reminiscent of updating money stream forecasts or flagging potential discrepancies for evaluate.
Knowledge safety and compliance
Defending delicate monetary information ought to be a precedence. Encryption ensures information safety each when saved and when transmitted.
Limiting entry to solely approved personnel, implementing Multi-Issue Authentication (MFA), and using information masking strategies scale back the chance of information breaches.
To take care of information privateness and consumer belief, at all times keep compliant with rules like GDPR, HIPAA, and SOC.
Third-party vendor administration
When working with exterior distributors, vet their safety protocols and compliance measures to make sure they meet business requirements.
Set up authorized agreements that define how information ought to be dealt with and conduct common assessments to confirm that distributors adhere to your safety and operational necessities.
Clear communication is crucial to keep up robust relationships and tackle any points promptly.
Monitor and evaluate
Frequently auditing information high quality is important for sustaining accuracy and compliance.
Use suggestions loops to refine AI algorithms and enhance processing accuracy. Maintain detailed logs of transactions and information modifications to make sure accountability and compliance with inside and exterior requirements.
Structured workflows, together with approval processes for information exports, can additional improve transparency and effectivity.
System upkeep and updates
Undertake a DevOps strategy to system upkeep, enabling steady updates and enhancements with out disrupting every day operations.
Implement an AI-powered documentation system that robotically updates SOPs and manuals primarily based on system modifications and consumer interactions.
Create a information graph of your processing system. It will permit workers to visualise interconnections between totally different elements and rapidly determine the affect of any modifications.
