23 statistics that highlight fraud risk in manual credit approvals

Updated on June 12, 2025

Manual credit approvals present a significant challenge for businesses trying to limit fraud risk. This article brings together 23 critical statistics to help companies understand where fraud is most likely to occur in manual credit processes and how it can impact financial outcomes.

Decision-makers will find valuable insights that highlight why manual reviews are risk-prone, often leading to inefficiency, higher error rates, and potential losses due to overlooked red flags. For a deeper dive into the dangers of outdated processes in the financial sector, consider reading about risks of manual document processing.

1) Manual credit approvals increase the chargeback rate significantly compared to automated processes

Businesses relying on manual credit approvals often see higher chargeback rates than those using automation. Manual reviews are slower and leave more room for human error, which can let more fraudulent transactions slip through.

Automated solutions with AI can cut down on both processing time and risk, leading to fewer chargebacks as detailed in automated vs. manual chargeback management. For more on manual review risks, see manual fraud review.

2) Average approval rates in manual reviews are lower due to human risk aversion

Manual reviewers tend to approve fewer credit applications because they are cautious about potential fraud. This careful approach often leads to lower approval rates compared to automated systems.

Companies may see increased protection, but lower approval rates can impact customer satisfaction and lost revenue opportunities. For details about approval and denial data, average approval and denial rates are key KPIs in fraud management.

See how manual credit approval risk factors influence business outcomes.

3) Denial rates tend to be higher when credit approvals are conducted manually

Manual credit approvals often lead to higher denial rates than automated systems. Human bias and inconsistent decision processes can impact outcomes, especially for minority and low-income applicants.

Research shows that denial rates can be affected by factors like subjective evaluations and discriminatory patterns, as described in this study on lending discrimination.

For more insights on how technology can improve accuracy, see our internal article on predicting credit card approvals.

4) Manual review processes add an average of several hours to credit decision times

Manual review steps can slow the credit approval process for businesses. Each flagged application must be inspected by staff, which can add hours before a final decision is made.

Companies might find that manual review times affect productivity, especially during busy periods. Automating more approvals can help reduce these delays. See how other firms manage manual fraud review to save time in this manual fraud review article.

5) Around 66% of institutions with over $10 billion in assets use manual input for credit memo data

A recent survey shows about 66% of banks holding more than $10 billion in assets still rely on manual input for credit memo information. Manual entry increases the chance of errors and inconsistencies, which can expose businesses to more fraud risk.

Reliance on outdated processes is especially concerning, given that larger banks report higher budgets and rising expenses for technology and staff, as detailed in the banking industry outlook. Companies seeking more insights into digital fraud trends can read about fraud risk in manual credit approvals.

6) Manual processing leads to inefficiencies, impacting transaction and loan processing times

Manual credit approval slows down transaction times because staff must handle paperwork, enter data by hand, and review documents multiple times. This creates delays and can cause backlogs, especially during busy periods.

Key tasks, such as consolidating and checking borrower data, become time-consuming and prone to bottlenecks. Many businesses experience delays in loan processing because data is copied from system to system, as shown in manual processes in lending.

Longer processing times increase the risk of missed business opportunities. For more about how inefficient manual document processing affects organizations, see this detailed explanation.

7) Financial institutions report that manual credit approval is a common bottleneck in compliance checks

Manual credit approvals slow down compliance checks at many financial institutions. Staff often spend extra time on repeated data entry and paperwork, which increases the risk of delays and errors.

These bottlenecks can become more severe when there are high volumes of loan applications or regulatory changes. Direct impacts include longer processing times and more room for mistakes during reviews, as noted in lending obstacles tied to manual processes.

When processes are inefficient, banks are exposed to higher risks. For more on risks of manual document processing in financial institutions, see vaultedge.com/resource/ungated/blog/the-4-unavoidable-risks-of-manual-document-processing-in-financial-institutions.

8) 3 out of 4 banks plan to invest in identity risk solutions to reduce manual fraud risk

Seventy-five percent of banks and fintech companies say they are planning to invest in identity risk solutions over the next year. These solutions help reduce fraud by improving how businesses verify customers and monitor risk at every step. Details can be found in the state of fraud benchmark report.

Manual credit approvals often expose organizations to higher fraud risks. Automated identity platforms can help banks lessen these risks by providing more reliable verification tools. For more tips, see their article on how banks can enhance fraud risk management programs.

9) The rate of fraudulent orders approved by manual reviewers remains a documented risk metric

Businesses are tracking the rate of fraudulent orders that pass through manual review. This rate is a key performance indicator and helps show whether the current fraud prevention process is working as intended.

Recent data emphasizes that even with skilled reviewers, some fraudulent transactions are still getting approved. For more detail, see how the rate of fraudulent orders approved by a manual reviewer can impact overall risk metrics.

Adjusting fraud risk analysis tools to better handle "in-between" orders can help lower this number, as described in manual review best practices.

10) Manual credit approvals contribute to increased operational costs due to resource intensiveness

Manual credit approvals require staff to review documents and verify information step by step. This labor-intensive process increases payroll costs and takes up more management time.

The need for trained fraud analysts and specialists adds extra expenses for businesses. Delays from manual reviews can also disrupt customer experiences and reduce efficiency.

Firms looking to reduce these risks may explore automation in mortgage lending for cost savings.

11) 50% higher likelihood of errors in credit risk assessment occurs in manual vs automated reviews

Manual credit assessments are more prone to human error than automated processes. One study found that mistakes are 50% more likely when staff handle approvals without automation.

This raises the risk of wrong decisions and increased fraud. For more about improving these processes, see automated credit risk assessments in lending and the credit risk review process.

12) Manual document processing exposes institutions to avoidable errors and fraud vulnerabilities

Manual document processing creates more chances for mistakes. Errors like typos or missed data can lead to serious financial risks for businesses.

These manual steps also make it easier for fraud to go undetected by bypassing basic controls. Manual handling increases exposure to loss of sensitive data, increasing both time and risk of fraud and errors.

Automated solutions can help reduce the risks of manual document processing and strengthen security.

13) Longer manual approval times correlate with increased risk exposure and customer dissatisfaction

Manual credit approvals cause delays that can leave businesses more open to fraud and other risks. Each extra day spent on approvals may give bad actors more time to exploit weak points.

Slow approvals also frustrate customers. Studies show that customer dissatisfaction rises when processing takes too long. Effective controls over the sales approval process help reduce these risks, as outlined in this risk control matrix for sales approval.

14) Manual reviews struggle to scale effectively with growing customer bases

Manual credit reviews become slower as the number of applicants rises. Each review requires time and trained staff, so it is hard to keep up when customer growth accelerates.

This process often leads to delays and higher costs for businesses. A report notes that manual reviews are impossible to scale efficiently when customer demand increases.

As more companies turn to technology, many see benefits from fraud scoring tools, which help reduce workloads on manual teams.

15) Manual credit approvals often fail to dynamically balance risk and customer sensitivity

Manual credit approvals rarely adjust fast enough to handle changing customer situations or risks. Staff may not have access to current data or the tools needed for real-time decisions.

This can lead to increased losses or missed business when the process can't react to customer needs. According to 4 Common Challenges of Manual Credit Decisioning, manual processes struggle to keep pace with market and regulatory demands.

More firms are considering automated solutions to help manage credit risk rating systems and better serve their customers.

16) 75% of financial institutions plan to reduce manual fraud touchpoints in the next year

Three out of four financial organizations expect to use fewer manual steps to spot fraud in the next 12 months. This shift aims to lessen human error and speed up approvals.

Investing in an identity risk solution is one way many banks and fintechs are addressing this. More details on these plans appear in the State of Fraud Benchmark Report.

To keep up with trends like automation and fraud risk, review 2024 financial fraud stats for banks.

17) Manual entry of consolidated borrower financials introduces significant error potential

Manual entry in consolidated borrower financials often results in mistakes, especially when information is transferred across different systems. Financial staff may miskey numbers or omit key data.

Studies show that reliance on manual processes increases human error rates, particularly when data must be copied into multiple records or documents. This is a known problem in the banking industry, with firms noting higher inaccuracy when they enter financial data manually.

Errors in financial statements can lead to poor lending decisions and lost revenue. For tips on reducing manual entry risk in banking, review the company’s finance and compliance guidelines.

18) Human manual review is necessary but should be complemented with automated fraud detection

Manual review can catch fraud that automated tools miss, making it a key step for businesses. However, relying on people alone brings higher labor costs and can slow down the process.

Combining manual processes with automated fraud detection systems helps reduce risk but also keeps operations efficient. Businesses that use both methods are better protected from mistakes and missed red flags during credit approvals.

For more on this topic, see how manual credit approval process impacts fraud risk.

19) Manual reviews increase delays in resolving suspicious credit orders, risking losses

Manual review processes add extra time to resolving suspicious credit orders. This delay is a major concern for businesses, as it can slow down transactions and create frustration for both staff and customers.

On average, reviewing a suspicious transaction manually takes about 5.6 minutes per order, based on manual review fraud survey data. High review volume only increases these delays.

Businesses seeking solutions should consider combining automated systems with strategic manual effort, as described in this guide to improving manual review performance.

20) Manual review accuracy is affected by reviewer fatigue and subjective risk tolerance

Manual credit reviews are influenced by human factors, including fatigue. When reviewers handle large volumes of cases, they may rush or overlook information, causing errors. Reviewer fatigue can slow the process and reduce effectiveness, as noted in research on reviewer fatigue in publishing.

Subjective risk tolerance is another challenge. Different reviewers may approve or reject identical applications based on their own risk preferences, which can impact consistency in manual fraud review and detection.

21) Institutions with high manual review rates report increased instances of fraudulent credit approvals

Organizations that rely heavily on manual credit reviews have seen a noticeable rise in fraudulent approvals. Manual processes often lack the speed and consistency of automated systems.

This increase has been confirmed by surveys such as the Federal Reserve Financial Services 2023 risk officer survey. Businesses interested in strategies to prevent check fraud can review the guide on check fraud statistics.

22) Fraud risk increases in manual approvals involving complex credit scenarios or offshore entities

Manual credit approvals become riskier when they involve offshore entities or situations where credit terms are more varied and detailed. These cases are harder to verify and often lack consistent oversight, opening up more opportunities for fraud.

Businesses dealing with offshore entity risks in credit approvals should take extra precautions. Updating procedures and training employees can create stronger defenses against threats.

For more on fraud risk in business processes, see the article on fraud risk assessment pdf.

23) Manual credit approval processes impede rapid credit decisioning needed for competitive lending

Manual credit approvals slow down how fast companies can make lending decisions. Delays can cause businesses to lose customers to competitors who offer quicker approvals. These slowdowns are especially risky in fast-moving markets.

Automating approvals helps address slow manual credit decisioning and gives businesses a sharper edge. For more statistics about manual credit approvals, visit steadlily.com/blog/manual-credit-approval-statistics.

Understanding the Impact of Manual Credit Approvals

Manual credit approval processes put businesses at higher risk for fraud and inefficiency. These methods struggle to keep up with new fraud tactics and often limit both the speed and accuracy of approvals.

Key Risk Factors in Manual Processes

Manual reviews depend heavily on human judgment. Staff may overlook small inconsistencies or fail to recognize well-hidden patterns linked to fraudulent applications. In addition, decision fatigue increases as volume grows, causing more errors or rushed approvals.

Physical paperwork and simple digital files are easier for fraudsters to forge or manipulate. For example, altered bank statements or fake identification can slip past a busy employee, increasing risk for lenders.

Manual processes typically rely on limited data. This not only slows down the credit approval timeline but also excludes people who may actually be creditworthy, as traditional risk models are usually narrow. According to timvero.com, these gaps can stop qualified borrowers from getting access to credit, further impacting business growth.

Fraud Detection Challenges

Detecting fraud in manual systems is time-consuming, and even experienced staff struggle to catch sophisticated scams. Fraud detection tools are often basic, such as checklist reviews or document comparisons, making it easier for advanced fraud schemes to go unnoticed.

Many fraud indicators, such as mismatched data points or suspicious transaction patterns, require cross-checking large volumes of data. Manual examiners cannot process information as quickly or as thoroughly as automated systems like those described in AI-powered credit scoring models enhance loan approval. This makes it harder to identify real-time threats.

Paper-based records, weak controls, and less oversight mean some fraudulent applications are only caught after losses have occurred. As fraud grows more technical, manual processes become even less reliable for protecting a business against financial losses and regulatory risks.

Mitigating Fraud Risk with Automated Solutions

Reducing fraud in manual credit approvals requires both strong process controls and the ability to analyze large amounts of transaction data. Automated solutions use technology to close gaps where manual checks can fail, improving both speed and accuracy for businesses.

Benefits of Workflow Automation

Automated workflows create standardized approval paths, which make it easier to spot outliers and prevent mistakes. By controlling who can access or change information, companies cut down on risks related to human error and insider fraud.

These systems track all changes, so companies have an audit trail showing exactly who approved, reviewed, or changed applications. This makes investigations faster when something goes wrong.

Key benefits include:

• Reduced manual processing: Lowers errors and fraudulent approvals.
• Stronger access controls: Employees only see what they need.
• Automatic alerts: Triggers warnings when something unusual happens.

The impact of workflow automation can be significant. For example, some organizations have seen a 71% decrease in payment fraud attacks after strengthening their accounts payable workflow.

Integrating Data Analytics

Automated fraud detection systems scan large sets of data in real time to flag signs of fraud. These solutions use data from many sources, including customer profiles, transaction history, and external fraud databases.

Advanced tools can spot patterns linked to risky behavior, like repeated credit applications from the same device or sudden spikes in approval amounts. Machine learning models adapt over time, catching new types of fraud faster than manual reviews.

Companies that use automated fraud detection software benefit from immediate risk alerts and better decision-making data. These analytics help teams spend less time on false positives and focus on real threats. For more tips on credit approval controls, see this guide on internal audit best practices.

Frequently Asked Questions

Businesses need to address fraud risks proactively, especially when handling manual credit approvals. Effective frameworks, accurate tracking of fraud trends, and established guidelines can help reduce the risk of loss and reputational harm.

What are the key components of the GAO Fraud Risk Framework?

The GAO Fraud Risk Framework outlines leading practices for managing fraud risk in federal programs. Its components focus on making a fraud risk profile, developing a strategy, implementing preventive controls, and monitoring outcomes. It recommends regular assessments and having clear policies to reduce vulnerabilities.

How has the incidence of credit card fraud changed over recent years?

Credit card fraud has grown with the use of digital payments and manual entries. Data shows that fraud losses in card transactions, especially those involving manually entered credit card information, are rising. This trend puts extra pressure on organizations that rely heavily on manual approval systems.

Which types of credit card frauds are most prevalent?

Card-not-present fraud remains a leading threat, often linked to online transactions or manual key-in at the point of sale. Other common methods include identity theft and account takeover. Businesses must closely watch transactions where physical cards are not used.

What does the COSO Fraud Risk Management Guide recommend for organizations?

The COSO Fraud Risk Management Guide advises companies to build anti-fraud programs that emphasize risk assessments, employee training, reporting channels, and oversight. Controls should be practical and matched to the company's size and risk appetite. Strong monitoring systems are essential to spot fraud early and take corrective action.

What does 'operationally required' mean within the context of fraud risk management?

'Operationally required' refers to specific processes or data that cannot be removed without disrupting daily business. In fraud risk management, it means only collecting and using information that is essential for functions like credit checks, reducing exposure to unnecessary risk.

What are the most significant risk factors for financial reporting fraud?

Weak internal controls, excessive focus on short-term targets, and lack of oversight can heighten financial reporting fraud risk. Institutions that rely on manual input for credit memo or use limited checks are especially vulnerable. Encouraging transparency and accountability can help prevent these kinds of fraud.

This post is to be used for informational purposes only and does not constitute formal legal, business, or tax advice. Each person should consult his or her own attorney, business advisor, or tax advisor with respect to matters referenced in this post. Resolve assumes no liability for actions taken in reliance upon the information contained herein.