Underwriting involves making sense of everything in the financing package: bank statements, tax returns, financial statements, sometimes years of them. Historically, processors and underwriters did this by hand, going line by line to build a complete picture before making a decision. Depending on the financing product and the complexity of the business, that review can take hours or days.

There are two primary methods to automate document data extraction: deterministic parsing or probabilistic AI.

• Deterministic parsing extracts a value because the document's structure defines where that value sits, following defined logic built around how a given document type is laid out. 
• Probabilistic AI, typically a large language model or similar pattern-based system, predicts a value because it resembles what usually appears in that position, based on patterns learned from the documents it was trained on.

Both methods can produce a number. Only one of them can tell you where that number came from. Run the same document through a deterministic system twice and you get the same output twice. Run it through a probabilistic system and you get its best guess, which can look correct without being correct.

For underwriting data feeding a DSCR calculation or a stacking check, that gap changes what the number means. A deterministic extraction traces back to the document itself. A probabilistic prediction traces back to a training distribution.

How MoneyThumb’s engine reads a document

Deterministic doesn’t mean matching a document against a stored template for that specific bank or statement format. For approximately 90% of our processing volume, MoneyThumb’s engine reads the PDF’s encoded content directly, including: the actual text objects, positions, and structure the file contains, and uses the relational positions of those elements (where a number sits relative to a label, column, a running total) to reconstruct the document’s layout. This process runs the same way regardless of whether the engine has seen that particular bank’s format before. 

Are there any exceptions?

Two document types are the exception:

• IRS tax forms: processed against the IRS’s published templates
• Application forms: processed against templates we build in-house

Everything else, bank statements in particular, don't depend on a template at all.

How each approach handles the same document

Where OCR and AI/LLM extraction introduce probability

Two different methods get lumped together under "AI parsing," and they carry different kinds of risk.

• OCR (optical character recognition) is required any time a document has no embedded text layer, meaning it's a scan or image-based PDF instead of a native PDF. OCR estimates which character a cluster of pixels most likely represents. That estimation step is unavoidable on image-based files and exists no matter whose pipeline runs it, MoneyThumb's included. It's a property of the file and often not a choice of method.
• AI/LLM extraction is a separate method that introduces probability even on documents that don't need it. In most cases, a multimodal LLM reading a PDF converts the page to an image first, runs OCR on that image, and feeds the resulting pixels and tokens into a model that generates a response. That means a clean, text-based statement - one MoneyThumb's engine would read outright - gets converted and re-derived probabilistically anyway once it enters an LLM-based pipeline. This adds several extra steps even when they’re not necessary.

Where probabilistic parsing can be risky

Errors in probabilistic system output can easily appear correct. Here are few examples how these false positives can cause downstream impacts in MCA and SMB underwriting:

• A misread deposit inflates apparent revenue. A model trained mostly on standard bank layouts misreads a subtotal on a credit union statement as a deposit, pushing calculated monthly revenue above what the business actually brings in.
• A recurring NSF fee reads as a routine withdrawal. Without a defined rule for identifying fee codes specific to that institution, the model classifies it as an ordinary transaction, masking a cash flow problem an underwriter needed to see.
• A second MCA position goes undetected. A model extrapolating from familiar statement formats misses a less common transaction description used by a smaller or regional funder, so a stacked position doesn't get flagged before funding.
• A factor rate or repayment schedule gets transposed. On a statement layout the model has seen infrequently, digits in adjacent columns get swapped, changing the effective cost of the deal without any indication that anything went wrong.

Where deterministic parsing closes the gap

The same scenarios play out differently against defined extraction logic:

• Balance reconciliation measures against a baseline. When a starting balance, transaction list, and ending balance don't reconcile, the system flags it immediately, whether that's a parsing issue or a sign the document itself was altered.
• New or uncommon layouts get parsed on structure, not guessed on resemblance. Because the engine reads relational positioning rather than matching against a stored format, a credit union or fintech statement it hasn't processed before still extracts correctly, without needing to have seen that exact layout first.
• Fee and transaction codes extract by defined category, not resemblance. A recurring NSF fee is identified because it matches a known code or pattern for that institution, not because it looks similar to other withdrawals.
• Every extracted field ties back to a specific location in the document. An underwriter reviewing a flagged transaction data will always see numbers directly sourced from the document.

The tradeoffs with deterministic parsing

Deterministic parsing isn't free of limitations. Here's where it's fair to push back, and what addresses each concern:

• It won't infer a value that isn't clearly present. This is by design. A missing or ambiguous field does not get filled with a best guess.
• Some document types will still depend on templates, and new template types take upfront work. IRS forms are processed against the IRS's own templates, and application forms against MoneyThumb's own. Building support for a new template type may take some initial work, but it becomes a permanent capability once complete. Bank statements make up the majority of underwriting documents and don't carry this limitation at all, since they're parsed structurally rather than matched to a template.
• A degraded or malformed document can still fail to resolve cleanly. A document with corrupted encoding or inconsistent structure may not parse with full confidence. This makes up an average of 2% across the millions of documents we process each month. When that happens, the engine can switch to OCR to complete the job instead of returning a partial or uncertain result. 

How does this work with Thumbprint?

The parsing layer that pulls transaction data out of a document (the numbers that PDF Insights customers use for cash flow analysis and reporting) applies the same extraction logic to the same document structure every time. That output doesn't shift unless it's to fix a reconciliation issue for that format moving forward.

ThumbPrint's fraud score works differently by design. Its inter-document model keeps learning from a growing population of verified statements, so a score on a given document can shift over time as MoneyThumb processes more documents from that institution. This creates a more accurate picture of the statement as the system's knowledge of an institution's real statement patterns grows, but does not change how the underlying transaction data gets extracted. The two systems solve different problems but only one of them needs to hold still.

Where MoneyThumb stands out from other tools

Many parsing tools that enter the SMB financing market lead with AI. MoneyThumb leads with deterministic parsing and leverages probability with scanned files where OCR is the only option. That ordering is deliberate. For underwriting data that feeds a funding decision, a number you can trace beats a number you have to trust.

Our pragmatism here is a differentiator, but it isn't the only one. MoneyThumb has parsed bank, credit union, and fintech-issued statements for over a decade. Our engine has encountered the full range of real-world document variation typically produced in the SMB financing industry, including the messy, adversarial, and one-off formats that trip up other tools without the same history behind them. A newer entrant may have a capable model, but there is no shortcut with exposure to a decade of processing volume.

• Deterministic-first by design, with probabilistic OCR used only as a fallback for image-based files, not as the default method for readable documents
• Structural parsing that handles new and uncommon layouts natively, with no dependency on bank or format templates, and no need to see a format before to extract correctly
• Long-tail depth from a decade of volume, across the credit union, fintech, and adversarial statement formats and edge cases most parsing tools haven't encountered at scale
• A document fraud signal that compounds with volume, since ThumbPrint's patented inter-document model gets sharper with every institution's documents it processes

Resources:

• ThumbPrint Core Concepts documentation
• PDF Insights
• https://www.moneythumb.com/blog/moneythumbs-thumbprint-secures-patent-for-fraud-detection-technology-in-document-authentication/
• https://www.moneythumb.com/blog/small-business-administration-sba-website-is-a-great-resource-for-lenders/
• https://www.moneythumb.com/blog/understanding-merchant-cash-advances-for-business/
• https://www.moneythumb.com/blog/lenders-make-instant-credit-decisions-using-thumbprint-from-moneythumb/

The post Deterministic Document Parsing vs. Probabilistic AI appeared first on MoneyThumb.

Let’s break down exactly how AI is influencing this sector and what it means for lenders and borrowers.

AI in Loan Underwriting: Smarter Risk Assessment

Traditional underwriting relied heavily on FICO scores, income statements, and limited credit history data. That model works but it misses nuance. AI-based underwriting systems analyze thousands of variables in seconds.

Machine learning models evaluate:

• Payment history patterns
• Transaction-level banking data
• Employment stability signals
• Spending behavior trends
• Education and career trajectory (in some models)

Instead of asking, “Does this borrower meet fixed criteria?” AI asks, “Based on similar borrowers, what is the probability of repayment?”

This approach allows lenders to:

• Approve more borrowers with thin credit files
• Reduce default rates through predictive modeling
• Price loans more accurately according to real risk
• Update models dynamically as new repayment data comes in

Platforms like Upstart have publicly reported that AI-driven models can reduce default rates while approving more applicants compared to traditional scoring systems.

From a lender’s perspective, that’s significant. Risk becomes measurable at a deeper level.

Faster Loan Approvals and Automation at Scale

One of the biggest visible effects of AI is speed. Online private lenders can now approve loans within minutes instead of days.

Here’s why:

AI systems automate:

• Identity verification
• Income validation
• Fraud screening
• Document processing
• Risk scoring

Optical character recognition (OCR) tools extract data from bank statements and tax documents. Natural language processing scans applications for inconsistencies. Behavioral analytics monitor user interactions during application.

Instead of a human underwriting team reviewing each file manually, AI handles the bulk of decisions instantly, escalating only edge cases for review. For lenders, this reduces operational cost. For borrowers, it improves experience and conversion rates. Speed matters in online lending. And AI delivers it.

Expanding Access to Credit

One of the most important shifts AI brings is broader financial inclusion.

Traditional credit models often reject:

• Gig workers
• Freelancers
• Young borrowers with limited history
• Immigrants without established credit records

AI can evaluate alternative data sources such as:

• Cash flow from bank accounts
• Utility payments
• Subscription payment consistency
• Income volatility patterns

Instead of focusing solely on past credit lines, AI models examine real-time financial behavior. That allows some borrowers previously considered “high risk” to qualify for loans at reasonable rates.

However, this comes with responsibility. If models are not carefully monitored, they can also amplify bias embedded in historical data. That’s where regulatory oversight becomes critical.

AI-Powered Fraud Detection in Online Lending

Fraud is a major threat in online private lending. Synthetic identities, stolen credentials, and application manipulation are common issues.

AI systems detect fraud through:

• Behavioral biometrics (typing speed, mouse movement patterns)
• Device fingerprinting
• IP pattern analysis
• Anomaly detection in financial data
• Cross-platform identity verification

Machine learning models flag inconsistencies that humans would never catch manually. For example, if a borrower’s transaction history doesn’t align with their claimed employment profile, the system may escalate the case.

Dynamic Loan Pricing and Risk-Based Interest Rates

AI enables dynamic pricing models that adjust interest rates based on highly granular risk predictions.

Instead of broad credit bands, lenders can price loans based on:

• Probability of default
• Loss given default
• Prepayment likelihood
• Macroeconomic indicators
• Sector-specific employment risk

In volatile economic periods, models can adjust in real time. For example, during economic downturns, lenders may tighten approval thresholds automatically.

This flexibility allows online private lenders to:

• Protect portfolio performance
• Maintain profitability
• Offer competitive rates to lower-risk borrowers

The days of static pricing tables are fading.

AI in Loan Servicing and Collections

The lending process doesn’t end at disbursement. Servicing and collections are equally important.

AI systems help lenders:

• Predict early signs of delinquency
• Identify borrowers at risk of missed payments
• Send personalized reminders
• Offer restructuring options automatically

Predictive analytics models analyze behavior changes, such as reduced account balances or spending shifts, to anticipate repayment stress. Instead of waiting for default, lenders intervene early. That reduces write-offs and improves customer retention.

Some lenders also use AI-powered chatbots for servicing inquiries, reducing support costs while maintaining 24/7 availability.

Customer Experience and Personalization

Online private lending is competitive. AI helps platforms differentiate through personalization.

AI systems can:

• Recommend loan products based on borrower profile
• Adjust loan amounts dynamically
• Offer refinance suggestions at optimal times
• Customize repayment schedules

Personalization improves conversion rates and borrower satisfaction. It also increases cross-sell opportunities.

From a marketing perspective, AI helps lenders target qualified leads more accurately, reducing acquisition costs.

Regulatory and Compliance Implications

AI adoption in lending raises serious regulatory questions.

Key concerns include:

• Algorithmic bias
• Explainability of credit decisions
• Fair lending compliance
• Data privacy
• Model governance

Regulators such as the Consumer Financial Protection Bureau (CFPB) and global financial authorities emphasize transparency in automated credit decisions.

If a borrower is denied credit, lenders must explain why. But complex machine learning models don’t always provide simple explanations.

This has led to growth in “explainable AI” tools that translate model outputs into understandable reasons for approval or denial.

Compliance teams now work closely with data scientists. AI doesn’t remove regulation it increases the need for structured oversight.

Data Privacy and Ethical Concerns

AI systems rely heavily on data. The more data they access, the more accurate predictions become.

But that creates ethical challenges.

Questions lenders must address:

• What data is fair to use?
• Should social media data influence loan decisions?
• How long should borrower data be stored?
• Are borrowers aware of alternative data usage?

Privacy regulations such as GDPR in Europe and evolving U.S. state-level laws are shaping how lenders deploy AI.

Trust is critical in financial services. Overuse of intrusive data can damage brand reputation.

Operational Efficiency and Cost Reduction

AI reduces operational costs in several ways:

• Fewer manual underwriters
• Automated document review
• Reduced fraud losses
• Lower call center staffing needs
• Improved collection recovery rates

For online private lenders operating on thin margins, cost efficiency directly affects profitability.

At scale, AI-driven platforms operate leaner than traditional lending institutions.

AI vs Traditional Credit Models: A Comparison

This shift explains why many fintech lenders are growing faster than traditional institutions.

Challenges AI Brings to Online Private Lenders

Despite benefits, AI introduces complexity.

Major challenges include:

• Model drift during economic shifts
• Bias embedded in training data
• Regulatory scrutiny
• High infrastructure costs
• Cybersecurity risk

AI systems require continuous retraining. A model trained in a strong economy may fail during recession conditions.

I’ve noticed that many lenders underestimate model monitoring. It’s not enough to build a model you have to audit it constantly. Without oversight, AI becomes a liability instead of an advantage.

The Future of AI in Online Private Lending

AI adoption will continue to grow. Key future trends include:

• Real-time income verification through open banking APIs
• Fully automated micro-lending
• Blockchain-integrated identity verification
• AI-driven embedded finance within e-commerce platforms
• Personalized credit limits adjusted dynamically

We’re moving toward an environment where credit decisions happen instantly in the background of everyday transactions.

For online private lenders, survival will depend on:

• Responsible AI governance
• Transparent decision systems
• Strong cybersecurity infrastructure
• Continuous model optimization

Those who balance efficiency with compliance will lead the market.

Final Thoughts

AI is fundamentally changing online private lending. It improves underwriting precision, speeds up approvals, reduces fraud, enhances collections, and lowers operational costs. At the same time, it raises new challenges around regulation, fairness, and data ethics. For lenders, AI is no longer optional. It’s a core infrastructure component. For borrowers, it means faster decisions and potentially broader access to credit but also increased reliance on algorithmic evaluation.

The real competitive edge will belong to lenders who combine advanced AI systems with responsible governance and transparent communication.

FAQs

How is AI used in online lending?

AI is used for underwriting, fraud detection, loan pricing, risk prediction, document processing, and collections management.

Does AI improve loan approval rates?

Yes, AI can approve more borrowers by analyzing alternative data while maintaining or reducing default risk.

Is AI in lending regulated?

Yes. Regulators require lenders to comply with fair lending laws, explain credit decisions, and protect consumer data.

Can AI replace human underwriters?

AI handles most standard cases, but complex or high-risk applications still require human oversight.

References

1. https://www.consumerfinance.gov
2. https://www.federalreserve.gov
3. https://www.mckinsey.com/industries/financial-services
4. https://www.bis.org
5. https://www.weforum.org
6. https://www.forbes.com/sites/forbesfinancecouncil
7. https://www.pwc.com/gx/en/industries/financial-services
8. https://www2.deloitte.com/us/en/pages/financial-services
9. https://www.brookings.edu/topic/financial-regulation
10. https://www.harvardbusinessreview.org
11. https://www.morningstar.com/bonds/why-ai-worries-about-software-are-hitting-private-credit
12. https://www.nb.com/en/global/how-ai-is-reshaping-credit-markets
13. https://true.ai/closing-and-beyond/
14. https://www.bloomberg.com/news/newsletters/2026-02-04/ai-s-lending-risk-getting-tougher-to-compute

The post How AI is Affecting Online Private Lenders appeared first on MoneyThumb.

Both large and small businesses are already at the forefront of this AI revolution, as technology can rapidly transform the way online businesses work. From enhancing customer experiences to streamlining operations, AI technology is helping businesses of all sizes achieve greater success. In this article, we’ll explore how AI technology can help online businesses both large and small, what the best options are for your business, and how you can go about implementing AI technology in your company.

What Is AI?

Although AI has been on the scene for quite a while, it’s often still shrouded in mystery for many business owners. In simple terms, AI is the ability of machines or computer systems to perform tasks that would typically require human intelligence.

AI systems are designed to mimic human cognition and decision-making processes, completing tasks such as learning, problem-solving, and perception. AI can be divided into two main categories: weak AI also known as narrow AI and strong AI also known as general AI.

 Narrow or weak AI is designed to perform a specific task, such as playing chess, recognizing speech, or driving a car. These systems are not capable of performing tasks outside of their specific domain and do not possess human-level intelligence. These are the kinds of AI that are most commonly used by online businesses both large and small.

General or strong AI, on the other hand, is designed to perform any intellectual task that a human can. This type of AI would be capable of understanding any intellectual task that a human is capable of performing. Strong AI is still largely a theoretical concept and does not currently have practical applications in business.

The Benefits of Using AI in Business

 Now you know what AI is, let’s take a brief look at some of the benefits it can bring to businesses both large and small.

• Increased Efficiency and Productivity - AI technology can help businesses automate and streamline tasks that were previously time-consuming and labor-intensive. This can free up employees to focus on more complex tasks and projects, ultimately increasing productivity and efficiency.
• Improved Accuracy and Precision - AI systems are designed to make decisions based on data and algorithms, which can lead to more accurate and precise results. This can be especially useful in areas such as data analysis, quality control, and risk assessment.
• Cost Savings - By automating tasks and improving efficiency, using AI technology means businesses save money on labor and operational costs. Additionally, AI systems can help businesses optimize pricing strategies and reduce waste, further contributing to cost savings.
• Improved Decision-Making - Making informed and data-driven decisions is what AI is trained to do. By analyzing large datasets and predicting trends and patterns, AI systems can provide valuable insights. These findings can inform your business strategies and even your decision-making process as a whole.
• Better Risk Management - AI technology can help businesses identify and mitigate risks more effectively. Whether this be trends in the market or suspicious customer behavior, AI can help you avoid common roadblocks and solve problems quickly.
• Competitive Advantage - By embracing AI technology, businesses can gain a competitive advantage in their industry. By optimizing pricing strategies, improving customer experiences, and making more informed decisions, AI will ensure your business stays well ahead of its

Types of AI Currently Used By Large and Small Businesses 

There are various types of AI technology that businesses use to enhance their operations. Some of these include:

1. Machine Learning

Machine learning is a subset of AI that allows machines to complete tasks using data and experience using that data. Using set algorithms, machine learning AI analyzes large data sets and makes predictions based on patterns and trends.

Businesses often use machine learning for predictive algorithms, which can personalize recommendations. This is especially helpful for e-commerce sites, as machine learning can tailor marketing messages and recommend products that are the most likely to convert.

2. Natural Language Processing (NLP)

NLP is a form of AI that allows machines to understand human language. It involves the use of algorithms to analyze and interpret both spoken and written language. Using these interpretations, NLP can then generate human speech, making it incredibly useful for businesses.

One way both large and small businesses in a variety of sectors make use of NLP AI is through customer service bots. These chatbots and virtual assistants can provide 24/7 customer support, then escalate customer queries to a representative if necessary. NLP can also create content, extract keywords for SEO, and translate pages if a business operates in more than one country.

3. Robotics

Robotics is the use of machines to perform tasks that would typically be done by humans. This includes tasks such as assembly line work, packaging, and shipping.

Robotics is most commonly used by large businesses with substantial production and manufacturing requirements. As for small businesses that operate mostly online, robots are useful for expansion, especially if your business requires warehouse storage and organization.

4. Computer Vision

Computer vision means AI technology can interpret and understand visual data. It involves the use of algorithms to analyze images and videos and help the AI make sense of what it is seeing.

Computer vision has several practical applications and is incredibly useful for small online businesses too.

Computer vision can categorize product images, check product images for defects in handmade items, create Augmented Reality products that customers can ‘try’ in their own homes, analyze customer behavior, and even spot fake reviews of bot activity on your site, protecting you and your customers.

How Can AI Help Your Business? 

 Now that you know about the different types of AI currently in use, let’s dive deeper into how AI can improve your business’s chances of success.

1. Personalization of Customer Experiences

According to Gartner, 37% of businesses utilize AI, and the most desirable outcome of AI in business is personalized customer experiences. AI can analyze customer data, including browsing history and purchase behavior, to create individually tailored recommendations, promotions, and pathways to purchase. This can lead to increased customer satisfaction and brand loyalty. For example, Amazon uses AI technology to generate product recommendations for each of its customers, based on their purchase history.

2. Streamline Operations

AI technology can also help businesses streamline their operations. As we’ve seen, AI-powered chatbots can handle customer inquiries and support tickets, freeing up customer service staff to focus on more complex issues. However, AI can also automate repetitive tasks such as data entry and order processing. This means your employees can save time and avoid errors.

3. Fraud Detection and Prevention

Another way AI can assist businesses is by detecting and preventing fraud. AI algorithms can analyze customer behavior and identify potential fraudsters, helping businesses to take action before any damage is done. For example, PayPal uses AI technology to detect fraudulent transactions. The company's AI system analyzes data such as purchase history and location to identify suspicious activity and prevent fraud from progressing.

4. Predictive Analytics

AI can also be used for predictive analytics, allowing you to anticipate future trends and customer behaviors. This means you can make informed decisions and plan for market fluctuations. For example, if you run an e-commerce business, you might use AI to analyze customer data and predict which of your products will be popular in the future. Using this information, you can optimize your inventory and marketing strategies accordingly.

5. Supply Chain Management

AI technology can also help businesses manage their supply chains more efficiently. For example, AI algorithms can analyze shipping times and inventory levels to optimize your supply chain. In addition, AI can identify potential disruptions in the supply chain and take action to prevent them. This means your business can avoid delays, reduce costs, and make sure your customers are satisfied.

6. Price Optimization

AI technology can also be used for price optimization, allowing businesses to set the best prices for their products and services. AI algorithms can analyze data such as demand and competition to determine the optimal price point for a product. For example, a hotel chain could use AI to analyze data on room occupancy rates and competitor prices to determine a competitive rate for each room. This helps businesses stay relevant in industries that see rapid change.

How to Implement AI in Your Business

Implementing AI technology in a business requires careful planning and consideration. The first step in implementing AI is to clearly define the problems that the technology is intended to solve. This may involve identifying tasks that are time-consuming or error-prone, or analyzing data to find patterns and trends in inefficiencies. Once the problem has been defined, the next step is to identify the appropriate AI technology to address the issue. This may involve researching different types of AI technology and determining which one is best suited to the problem at hand.

AI technology is complex and requires a deep understanding of machine learning algorithms, data analysis, and software development. If you want to implement AI technology in your business but don’t have access to in-house expertise, it may be best to hire an expert to assist with the implementation process. While hiring an AI expert may mean paying an upfront cost, it can ultimately be more cost-effective than attempting to implement AI without the necessary expertise, which can result in errors, delays, and additional costs.

In Conclusion…

AI technology has the potential to revolutionize the way online businesses, both large and small, operate. From personalizing customer experiences to optimizing prices, AI can help businesses achieve greater success, maintain and improve brand reputation, and cut costs.

By embracing AI technology, your business can stay ahead of the competition and provide better experiences for your customers. To implement AI, outline the problems facing your business and do your research. If you don’t have any employees who specialize in AI, it can be highly beneficial and cost-effective to hire an expert.

Sources

• https://www.businessgo.hsbc.com/en/article/how-artificial-intelligence-can-help-your-business
• https://www.techtarget.com/searchenterpriseai/definition/AI-Artificial-Intelligence
• https://www.bmc.com/blogs/machine-learning-can-benefit-business/
• https://www.maguiretraining.co.uk/blog/a-brief-introduction-to-nlp-for-business/
• https://www.avenga.com/magazine/computer-vision-value-for-businesses/
• https://levity.ai/blog/8-uses-ai-business
• https://www.techtarget.com/searchenterpriseai/feature/6-key-benefits-of-AI-for-business
• https://www.godatafeed.com/blog/how-amazon-uses-ai-to-dominate-ecommerce
• https://www.paypal.com/us/brc/article/paypal-machine-learning-stop-fraud
• https://www.simplilearn.com/tutorials/artificial-intelligence-tutorial/artificial-intelligence-applications

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Artificial intelligence will not eliminate accountants

“Having machines to do all these tedious and repetitive tasks could sound scary for many accountants because they are also very time-consuming and thus very lucrative,” explained Stephanie Weil, CEO of Accounteam, a Silicon Valley-based accounting firm. “However, if the AI system is well configured, it can eliminate accounting errors that are generally hard to find and thereby reduce our liability and allows us to move to a more advisory role.”

Yes, AI is going to make your job as an accountant easier. The world of accounting is just the latest in a series of industries being affected by the rapid increase in the use of artificial intelligence. Bill Gates even referred to the rise of artificial intelligence as computer science's "Holy Grail." After many failed efforts in the past, the accuracy and speed of today's artificial intelligence are much improved.

The post Why Artificial Intelligence is the Future of Accounting appeared first on MoneyThumb.