VIn at present’s complicated 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 knowledge.
For companies processing 1000’s of financial institution statements each day—from insurance coverage firms to monetary establishments, financial institution assertion processing presents a problem and a fair greater alternative for automation.
Conventional handbook processing and reconciling, which consumes a median of 10-12 hours per week, is not an possibility. 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 establishing an environment friendly financial institution assertion processing system and share finest practices to rework this knowledge flood right into a strategic asset.
What’s financial institution assertion processing?
Financial institution assertion processing is extracting and analyzing monetary knowledge on financial institution statements.
- It includes extracting key particulars from financial institution statements, akin to transaction quantities, dates, descriptions, account balances, payee names, account numbers, and transaction varieties (e.g., debit or credit score, and many others.
- The financial institution extract data is then transformed right into a structured format for these and different accounting operations.
- The extracted knowledge is then despatched for financial institution assertion evaluation, additional processing, and accounting.
Financial institution assertion processing is important 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 sooner and extra precisely by automating transaction knowledge extraction, categorization, and evaluation. The result’s improved effectivity and scalability in monetary operations, lowering 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 finest practices for every step:
Gathering financial institution statements
Financial institution statements are available numerous codecs, akin to e-mail attachments, PDFs, bodily copies, or spreadsheets, every requiring a special strategy.
Historically, accountants and bookkeepers manually enter knowledge 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 troublesome 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 routinely extract pertinent data from financial institution statements utilizing machine learning-enhanced optical character recognition (OCR) expertise.
These instruments can determine key particulars akin to transaction dates, quantities, and descriptions, lowering the effort and time spent on handbook knowledge entry.
These instruments come in several codecs, like template-based knowledge extraction instruments, and superior AI-powered OCR software program.
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The price of utilizing template-based knowledge 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.
Nevertheless, in case you obtain financial institution statements in several codecs, languages, currencies, and templates, take into account investing in an automatic AI-based knowledge 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 complicated or ambiguous transactions.
Reconciliation
This step includes matching the extracted financial institution assertion knowledge with the corporate’s inner monetary information.
Guide reconciliation is a sluggish course of that includes accountants or finance groups evaluating 1000’s of transactions throughout a number of statements and accounts. EY reviews that AI reduces reconciliation errors by as a lot as 90%, lowering the probability 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 forex transactions or intercompany reconciliations.
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Create rule-based workflows that enable AI to deal with repetitive duties however contain human intervention when AI flags a posh exception or a high-risk anomaly.
Discrepancy identification
Discrepancy identification is a important side of financial institution assertion reconciliation. Throughout reconciliation, any mismatches are flagged for additional overview.
Routine discrepancies akin to timing variations (e.g., uncleared checks and financial institution charges) are widespread, 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 cut back fraud-related losses by 30-40% from discrepancies akin to duplicate invoices or suspiciously giant funds.
Changes
As soon as the accounting workforce identifies and explains discrepancies, they make the required changes. These changes be certain that discrepancies between financial institution statements and inner information are resolved promptly and precisely.
With pre-defined guidelines, AI can deal with routine changes, akin to small variances in quantities or forex conversions, by routinely updating the accounting system as soon as these changes are validated towards historic knowledge.
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Arrange predefined guidelines for changes as a consequence of widespread discrepancies, akin to rounding errors or clearing delays, in order that AI can routinely resolve them or flag them as anticipated with out human intervention.
Prioritize discrepancies primarily based on danger and use AI to categorise them into low, medium, and excessive precedence.
Transaction evaluation
After reconciliation, companies typically analyze the transaction knowledge for insights.
Accountants overview particular person transactions to make sure they align with inner 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 routinely apply the right trade charges primarily based on the transaction date and cut back the possibilities of miscalculations as a consequence of fluctuating trade charges.
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Organising automated classification and categorization of transactions primarily based on predefined guidelines and historic knowledge can considerably enhance the financial institution assertion evaluation.
Reporting
Lastly, the processed knowledge is summarized into reviews highlighting the group’s monetary standing. These reviews can embody insights on money move, expenditures, and general 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 reviews. These instruments can pull knowledge straight from reconciled financial institution statements and generate reviews with minimal handbook enter, guaranteeing consistency throughout reporting intervals.
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Utilizing NLP instruments to generate simple-language explanations of monetary reviews might help construct an ideal narrative. These reviews ought to spotlight key tendencies, variances, and insights to make reviews extra accessible to non-financial stakeholders.
AI-powered applied sciences in financial institution assertion processing
Synthetic Intelligence (AI) has remodeled financial institution assertion processing, making it sooner, extra correct, and able to dealing with giant knowledge volumes. AI-powered programs are revolutionizing how companies categorize transactions, detect fraud, and keep monetary accuracy.
This is a more in-depth take a look at the AI-enhanced applied sciences that play a key function in fashionable 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 akin to transaction quantities, dates, descriptions, and account numbers, no matter formatting complexity (tables, logos, and many others.).
AI-driven OCR is rather more superior than conventional OCR and might extract key data with as much as 99% accuracy. As an example, platforms like Nanonets can extract knowledge from various codecs and languages seamlessly.
The AI engine reads and organizes knowledge 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 knowledge administration.
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Key profit: Automating OCR processes can cut back handbook reconciliation time by as much as 90%, permitting companies to deal with 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.
In contrast to 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 follow, this permits for extra environment friendly monetary administration and correct categorization of bills, serving to companies keep correct budgets, determine spending patterns, and optimize assets.
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Key profit: NLP instruments can cut back the handbook effort of categorizing transactions by as much as 80%, considerably bettering the accuracy of monetary reporting.
Sample recognition and fraud detection utilizing ML
Machine Studying (ML) fashions analyze historic transaction knowledge to detect fraud and acknowledge patterns in spending habits. ML instruments repeatedly study from new transaction knowledge, enhancing their potential to flag anomalies that deviate from established patterns.
For instance, if a buyer usually makes small purchases from a well-recognized vendor however instantly has a big transaction at an unknown vendor, the system may flag it as uncommon.
ML-driven fraud detection programs provide 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 danger administration and cut 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 akin to accounting software program, ERP platforms, and databases. With Open Banking and API integrations, companies can straight join their financial institution assertion processing instruments with these programs, guaranteeing seamless knowledge move.
Instruments like Nanonets combine with standard platforms like SAP, Xero, Sage, Netsuite, and QuickBooks, enabling computerized knowledge extraction and direct import of key monetary data into accounting programs. This eliminates knowledge silos, reduces handbook knowledge entry, and ensures consistency throughout monetary reviews.
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Key profit: Companies can cut back operational prices by 30-40% by automating monetary knowledge move between programs by way of API integrations.
Automated reconciliation
AI enhances reconciliation by routinely evaluating knowledge from financial institution statements with inner monetary information (e.g., common ledgers). AI algorithms match transactions primarily based on particulars like dates, quantities, and payee names, flagging discrepancies for handbook overview.
By automating reconciliation, companies save time and cut back the probability of human errors, guaranteeing that their monetary statements are correct. This course of is important 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 bettering accuracy and transparency in monetary reporting.
Superior analytics and predictive insights
With real-time processing, AI-enabled instruments can ship on the spot insights into monetary knowledge. Think about with the ability to spot tendencies as they occur—this implies you possibly can funds extra precisely and put together for future bills with out the guesswork.
AI doesn’t simply crunch numbers; it might probably additionally analyze buyer habits by way of sentiment evaluation. By taking a look at transaction knowledge and buyer interactions, AI helps uncover what prospects assume and really feel. This perception lets you 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 move and monetary well being.
Contextual studying for steady enchancment
AI programs evolve with contextual studying, adapting to the information they course of and the suggestions they obtain. For instance, if a transaction is misclassified (e.g., private expense versus enterprise expense), the person can appropriate it, and the system will study from this correction, bettering future categorizations.
With extra knowledge and constant suggestions, AI fashions develop into more and more correct over time, minimizing handbook intervention. Companies implementing AI-powered options profit from programs that frequently enhance, lowering reliance on human oversight and enhancing general effectivity.
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Key profit: Over time, contextual studying can cut back handbook changes by as much as 60%, making a extra autonomous monetary administration course of.
The way to arrange an automatic financial institution assertion processing workflow
Right here’s a step-by-step information to establishing a financial institution assertion processing workflow utilizing Nanonets, an AI-powered knowledge extraction software:
Import all financial institution statements
- Join on app.nanonets.com free of charge and choose Pre-built financial institution assertion extractor.
- Acquire and import all of your financial institution statements from numerous sources, akin to Dropbox, Google Drive, E mail, Zapier, and OneDrive.
You may also arrange an import block primarily based on particular triggers, such because the arrival of a brand new file in your cloud storage.
Customise knowledge actions
- Arrange superior conditional knowledge motion steps to your financial institution assertion processing, akin to –
- Evaluation the extracted knowledge and customise your output by preserving 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’ll be able to obtain or export the ultimate ends in totally different codecs, akin to CSV, XML, Google Sheets, or Excel, or create a shareable hyperlink to share with the workforce.
- 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, akin to 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, take into account the next finest practices:
Integration with current monetary programs
Create a digital ecosystem by integrating your automated financial institution assertion processing instruments with current monetary programs.
This integration ought to transcend easy knowledge switch; intention for clever interactions the place processed assertion knowledge routinely triggers related actions in your accounting software program, akin to updating money move forecasts or flagging potential discrepancies for overview.
Knowledge safety and compliance
Defending delicate monetary knowledge needs to be a precedence. Encryption ensures knowledge safety each when saved and when transmitted.
Limiting entry to solely licensed personnel, implementing Multi-Issue Authentication (MFA), and using knowledge masking methods cut back the chance of knowledge breaches.
To keep up knowledge privateness and person belief, all the time 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 knowledge needs to be dealt with and conduct common assessments to confirm that distributors adhere to your safety and operational necessities.
Clear communication is important to take care of robust relationships and tackle any points promptly.
Monitor and overview
Repeatedly auditing knowledge high quality is significant for sustaining accuracy and compliance.
Use suggestions loops to refine AI algorithms and enhance processing accuracy. Hold detailed logs of transactions and knowledge adjustments to make sure accountability and compliance with inner and exterior requirements.
Structured workflows, together with approval processes for knowledge 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 each day operations.
Implement an AI-powered documentation system that routinely updates SOPs and manuals primarily based on system adjustments and person interactions.
Create a data graph of your processing system. It will enable workers to visualise interconnections between totally different elements and rapidly determine the affect of any adjustments.