More than half of all companies in the financial services industry rely on AI fraud detection. Read through this comprehensive guide to find out why.
What's the one factor the finance industry relies on most from its customers?
As a bank, you lend to account holders, trusting they'll pay you back. And as an insurance agency, you provide premiums to folks, trusting they'll be honest in their claims.
Without trust, the financial sector erodes, making it tougher for people to get the financing they need for business and personal endeavors.
Now that most financial applications, claims, and transactions occur digitally and faster than ever before, businesses are more exposed to potential fraud. Without a keen eye and special knowledge of a customer, it becomes challenging to detect and deter at scale. This is why 58% of the financial services industry relies on AI fraud detection.
The question is: Does AI fraud detection work? And if so, how can you implement it into your organization to reduce the instances of fraud? Let's find out.
One of the main reasons the financial industry uses AI in fraud detection is that it can find minute details that many humans will miss, costing companies thousands (if not millions) in revenue losses due to fraud.
Here's an example:
A customer applies for a loan using your automated underwriting system. You're pinged to review the application and uploaded documents.
There are bank statements, tax documents, a driver's license, and a filled-out form. At first glance, things look great. The address matches their license, and the tax and income documents show healthy finances.
An ideal candidate for one of your loans—except the bank and tax documents are forged. And this could be happening hundreds of times a day. Even top experts in the art of identifying specific details about various bank statements can’t always keep up with increasing volumes of applications coming through their systems each day.
On top of that, sophisticated image editing tools make it almost (if not entirely) impossible to detect alterations with the naked eye. This is where AI fraud detection becomes imperative. Without it, you risk approving an applicant who will take your organization's funds and disappear into the night.
If loan fraud occurs often enough, it can sink your business.
Before artificial intelligence, people had to rely on old-school methods (e.g., magnifying glasses and hours each day scouring over and comparing documentation). A time-consuming method that doesn’t scale with businesses working with a diverse, global, and mainly digital customer-base.
Rule-based fraud detection is another method that relies on patterns to work. Humans program these rules into the system and the AI sorts through it to make decisions, resembling human intelligence. But to work, it needs a set of facts and rules for manipulating that data. The problem: it can't "learn" new patterns. This makes the system vulnerable to new scenarios it never learned. The solution: machine learning AI.
Machine learning-based fraud detection extends beyond the initial algorithms created to find signals that indicate fraud. There are two ways this is possible—supervised and unsupervised machine learning (ML).
"Supervised and unsupervised models are essential in fraud detection and must be included in comprehensive, next-generation fraud strategies. Each transaction is classified as either fraudulent or non-fraudulent. The models are trained by ingesting massive amounts of labeled transaction data to uncover patterns that best depict legal activity. Unsupervised models identify unexpected behavior when labeled transaction data is sparse or non-existent. Self-learning must be employed in these cases to identify patterns in the data that standard analytics have missed." — David Reid, Sales Director at VEM Tooling
So how do AI and machine learning work together to reduce fraud risk? According to Sammy Belose, an expert in ERP and business management software, AI and machine learning excel at detecting financial fraud.
It starts with machine learning algorithms collecting, analyzing, and segmenting data, and then extracts required features (e.g., numbers, strings, and graphs for pattern recognition).
Machine learning models use training sets to predict fraud probability and improve the accuracy of fraud detection. So collecting and analyzing vast amounts of data is critical for AI and ML algorithms. The more data they receive, the better they get at building better fraud models.
The second stage of building a ML model is feature extraction to determine legit and fraudulent customer behavior patterns. For example, customers’ identity, network, location, orders, and chosen payment method. This list of investigated features will differ based on how complex the fraud detection system is.
Then a training algorithm launches, setting rules for an ML algorithm that will identify legitimate and fraudulent transactions. After the training is over, it generates an improved ML algorithm for detecting fraudulent activities. This algorithm can detect frauds in less time but with high accuracy.
The beauty of machine learning is that it uses allows businesses to detect fraud faster and at scale (compared to using humans alone). However, you need a platform that uses the right algorithm, based on your industry and needs.
Machine learning algorithms use historical fraud patterns to learn and recognize them in the future. Plus, it can identity fraud traits that go undetected by people. But there are many ways to build the algorithms.
Below are several examples.
Rule-based algorithms look for specific characteristics of known fraud, but rely on human knowledge to spot suspicious events. These algorithms are easy to implement but require constant updating because they only detect what happened before.
Fraud detection systems using rule-based approaches are also prone to false positives. With the increase in the number and size of customer databases, manual intervention is required to correct the errors.
Machine learning algorithms also learn from past experiences, but don't rely on human knowledge to detect fraud. Instead, it analyzes large volumes of data to find patterns that indicate fraud. These algorithms can also make predictions about future outcomes.
Machine learning systems reduce false positives for transactions, since it automatically detects patterns in large volumes of streaming transactions.
Supervised learning fraud detection algorithms require model training using labeled (or tagged) data. You label the data manually or through an automated process. And then feed the labeled data into the model to create a prediction. Supervised learning algorithms can handle new types of fraud and unknown fraud scenarios.
Supervised learning models are trained on tagged data: When fraud occurs, it's tagged. Fraudulent transactions are fed into the model to train it. The accuracy of the model's output relies on how well organized your data is.
Unsupervised learning algorithms don't have labels. They don't need to know what's normal or abnormal. Instead, they analyze unlabeled data and identify clusters of similar items by discovering hidden structures within the data.
The accuracy of an unsupervised learning algorithm depends on how well the data is prepared. If there are no labels, the algorithm will try to group similar items together. But if you have too much noise, then the algorithm may group unrelated items.
An unsupervised learning model learns by itself, analyzes available data, and tries its best to find similar and unique patterns. These models help detect fraudulent activities. Both supervised and unsupervised learning models can be used independently or together for fraud detection.
Deep learning algorithms are neural networks that work at multiple levels to extract features from raw data. The deep learning algorithm has many layers and nodes. Each node performs a specific task like feature extraction, classification, and so on. The deeper the network, the more complex the problem becomes.
Deep learning algorithms use backpropagation to adjust the weights and biases of each layer. Backpropagation adjusts the weights and biases of each node based on the error between the actual outputs and desired outputs.
Deep learning fraud detectors can handle huge amounts of data and perform real-time analysis.
For example, a deep learning fraud detector can look at millions of transaction records and spot unusual behavior. Deep learning algorithms can also perform pattern recognition and classify transactions as either legitimate or fraudulent.
It does this by analyzing the content of the transaction, such as the amount, type, and source of payment. It can also examine the time stamps and other factors, and whether someone is trying to steal credit card information, commit identity theft, or make unauthorized purchases.
A deep learning fraud detector can even predict which transactions might be fraudulent before they occur.
Artificial intelligence reduces human error and detects flaws at scale. So it's no surprise banks are adopting AI and ML to catch suspicious transactions in real-time. One report shows 75% of banks using AI do so for fraud, and 88% of those that don't plan to within the next year.
Out of the banks employing AI, 60% say it's their most important anti-fraud tool. But how are they using it? Most adopt AI to detect fraudulent transactions. This is critical since most banks report that transaction fraud increased between 2020 and 2021. But with AI tools in place, 98% say it's successful in detecting fraud. Around 27% are coupling AI with rule-based algorithms and 6% are using it with data mining.
But this isn't something done entirely in-house—92% of banks outsource the systems used to detect transaction fraud to third-party providers. Why? Because keeping up with rapid technology advancements and fraud behaviors requires time and expertise, which they lack.
AI fraud detection doesn't just assist with deceitful transactions. It also helps with loan applications from bad actors. Scammers frequently submit applications using stolen personal information, such as names, birth dates, addresses, and photo IDs.
Others may use fake bank statements and manipulated financial documents to get approved for a loan. With artificial intelligence platforms like Inscribe, banks and credit card companies can accept applications with confidence.
It works by scanning documents to analyze metadata and pixel-level information to ensure the integrity of the document. Plus, the AI uses known legitimate documents (say, bank statements from specific institutions) to find variations in fonts and layouts.
The software recognizes personal data and classifies documents like utility bills, pay stubs, and tax documents. It's the second (or third) pair of eyes needed to limit fraud during application processing.
Insurance companies spend days to weeks assessing a single claim for potential fraud, poring over cases with property damage, car accidents, and unemployment claims.
So to reduce time and human error, insurers needed something beyond human-knowledge-based AI. For example, machine learning models that use semantic analysis to detect fake claims.
Machine learning uses enhanced algorithms to find inconsistencies in evidence and detect falsified documents. ML algorithms also flag duplicate claims and overstated repair costs—something that can take weeks to do (sometimes to no avail).
Financial institutions are becoming technology-based organizations. Finance tech, or Fintech, is the future of banking, especially as more companies go digital.
A lot of this requires using AI technology to speed up application screening and approval. This is particularly true since the onset of the pandemic, which caused a shift in consumer behaviors and priorities.
Numbers show 54% of financial service institutions with over 5,000 employees adopted tech into their operation. Unfortunately, cybercrime also increased by 74% during the same time.
The increase in real-time transactions calls for an AI solution to determine the authenticity of transactions within milliseconds.
"We predict that AI will continue to push its way into more critical functions within the financial services industry. For example, we’re seeing AI-driven credit underwriting become more popular in unexpected places such as smaller regional lenders and credit unions." — Mike de Vere, CEO, Zest AI
Using artificial intelligence to safeguard your business from fraud isn't a fad—it's the future. This is especially true in the financial sector. If you're a bank, creditor, insurance company, or even property manager, having a way to detect application fraud is critical.
Inscribe’s platform uses AI, ML, and so much more to help detect millions in fraud monthly for our customers. Sound like a tool you need in your tech stack? Then talk to one of our experts today.