Bank Statement Extraction

How does artificial intelligence help banks reduce fraud?

Fraud detection using AI in banking swiftly recognize irregularities, analyze patterns, and assign risk scores to customers. Read the blog to learn about more AI fraud detection in banking and how to use it for fraud prevention.

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How does artificial intelligence help banks reduce fraud?

Artificial intelligence (AI) is changing the way banks detect fraud. Powered by machine learning algorithms, financial institutions can identify and prevent fraudulent financial activities before they cause serious damage. 

While traditional means of bank fraud detection were more reactive, predictive analytics and machine learning algorithms enable financial institutions to adopt a more proactive approach to anomaly detection. 

Let's take a closer look at how AI reduces bank fraud and understand the benefits and limitations that come with it.

Use cases of AI for bank fraud detection 

Financial institutions can strengthen their internal security protocols, streamline corporate operations using the insights from AI systems, and prevent fraudulent activities using machine learning algorithms. 

1. Identify Irregularities

In the traditional banking system, auditors and banking officials would spend months trying to analyze fraudulent transactions. Now, machine-learning models can identify financial transactions in real-time and flag the ones that are even remotely suspicious. 

Enhanced anomaly detection is possible due to the algorithm’s massive data-crunching capabilities. AI-enhanced banking systems analyze vast volumes of data, including transactions, historical scam cases, and behavioral biometrics.

Intelligent document processing software is capable of extracting unstructured data from customers to identify potential fraud indicators or forged documents. The presence of ML models in banking systems makes organizations more proactive with their fraud detection measures. 

2. Pattern recognition

Cybercriminals often have the ability to come up with ingenious hacks to defraud banking institutions. The archaic rule-based algorithms cannot recognize new attack patterns and fail to protect the banking systems from these attacks. In such cases, banks need software that can continuously analyze these hidden patterns and prepare responses for new situations. 

ML models try to self-learn and progressively become better at detecting fraud. Once the machine is set up, it analyzes every transaction for anomaly detection without relying on human labor. 

3. Risk scoring

AI-driven risk scoring models use modern metrics, like customer behavior, historical repayment patterns, and current transaction patterns. Assigning the score to the customers helps bank officials evaluate their profiles better and empowers institutions to proactively deal with suspicious triggers. 

Risk scoring helps the bank constantly monitor high-risk cases and protect customers’ interests while reducing any disruptions caused to genuine transactions. 

4. Biometric authentication

Advanced banking systems rely on biometric authentication, such as facial recognition and fingerprint scans, to authorize high-value transactions. AI leverages individual customer features to verify their identities and reduce the risk of unauthorized transactions. Biometric authorization also offers the customer a more convenient and secure method of accessing their accounts. 

Benefits of Using AI to reduce fraud

Currently, even the best AI programs can not fully replace a team of professional bankers. However, it can drastically improve their workflows and reduce the time taken to identify scams. Let’s have a closer look at all the benefits offered by intelligent anomaly detection systems. 

1. Increased accuracy

AI detection systems are much more powerful, intuitive, and accurate when compared to rule-based software. The newer detection software significantly reduces false positives and increases accuracy. For comparison, rule-based credit card fraud detection software has a false positive rate of 30-70% while intelligent systems have an accuracy rate of more than 90%.

2. Regulatory compliance

Banks have internal compliance teams that ensure all financial transactions are done according to government and banking regulations. But regulatory compliance is an extensive task and requires constant scrutiny and an in-depth understanding of the laws. 

So while AI cannot replace human input in this department, intelligent compliance systems fastrack the process using deep learning models. NLP and machine learning algorithms review regulatory and compliance guidelines and improve the decision-making skills of the internal teams. In addition, it ensures all transactions and operations conform to these laws. 

In short, the application of AI in regulation monitoring helps internal teams deploy their compliance strategies more swiftly and effectively. 

3. Real-time detection

One of the biggest benefits of using intelligent, or AI, fraud detection systems for banks is their ability to instantly check the extracted data’s authenticity. It compares the data and financial transaction history with available datasets to check their validity.  And, since this happens in real-time, it speeds up detection. 

In fact, real-time bank fraud detection is also applicable to the identification of forged documents. Intelligent document processing (IDP) software extracts the data from these documents and compares it with the available information in the public database systems. It prevents people and organizations from using counterfeit documents to secure large loans. 

4. Adaptability

The machine learning algorithms in modern banking systems continue to learn and adapt to new threats and the detection of fraudulent activities. When the system generates false positives, it requires input and some recalibration from the employees. The system immediately incorporates the new changes and continues to reduce fraud with even faster and more accurate detections. 

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Limitations of AI in banking fraud prevention

Artificial intelligence in banking comes with certain limitations that can be easily tackled with simple solutions. Here are four limitations of intelligent fraud detection systems, along with solutions to fix them. 

1. Bias in machine learning algorithms

Algorithmic bias is a legitimate concern when deploying ML algorithms in banking systems. If ML model training uses biased data sets, it produces discriminatory outcomes. For instance, the AI model can repeatedly snub or target a particular racial group due to biased inputs. 

This limitation is easily addressed by curating and using diverse datasets while training the systems. Diverse datasets ensure the AI system remains fair and transparent while evaluating available information. 

2. Implementing AI in legacy systems

Financial institutions with legacy systems can find it challenging to switch over to AI-powered software. Complex digital infrastructures and compatibility issues plague these banks when they are trying to migrate to a new technology stack.

Although not challenging, implementing new systems or data migration requires planning and consulting with AI and IT specialists. 

3. Discussion of regulatory frameworks 

Anomaly detection is no easy task and requires the application of a comprehensive regulatory framework. Compliance and audit teams need to establish strict ethical standards to ensure the transparent operation of the software’s adaptive capabilities. 

The banks need to outline privacy regulations, define data usage, and establish parameters to make the system conform to government-mandated regulations. 

4. Data privacy and security

The transactional data of financial institutions and their customers is highly sensitive. Any data leakage can land organizations in a whirlwind of lawsuits and penalties. Some companies are still skeptical about sharing such information with third-party intelligent bank fraud detection systems. 

To prevent any security breaches due to third-party installations, banks need to extensively invest in advanced digital infrastructure and implement stringent data protection regulations to protect themselves. 


Docsumo helps banks and financial institutions capture customer data with over 99% accuracy. Arbor Realty Trust, a commercial lender, noticed a 95% straight through processing rate that helps them avoid manual risk assessment. Smart AI, along with predictive analytics, helps lenders make rapid decisions. 

Verifying data from multiple documents, like bank statements and W2 forms, becomes easy. The APIs can be trained to suit your loan approval workflows and reduce any portfolio risk. Eventually, the APIs will help reduce false positives. Again, the intelligent APIs can be connected to traditional file transmission methods as well as modern banking features. Docsumo maintains complete traceability, ensuring easy anomaly detection throughout the process. 

To revamp your fraud detection and enhance your lending workflows, try out Docsumo's 14-day free trial

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Pankaj Tripathi
Written by
Pankaj Tripathi

Helping enterprises capture data for analytics and decisioning

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