Category Archives: Machine Learning

Machine Learning In Banking

Utilizing Machine Learning In Banking To Prevent Fraud

Machine Learning (ML) is a vital tool for fraud detection in banks. It can spot potential fraud by examining patterns in transactions and comparing them with known fraudulent activity. It uses algorithms to identify these patterns, which are then used to predict whether or not a transaction is fraudulent. These algorithms are trained using historical data, so they can only identify patterns in existing data and cannot learn new ways as they occur. 

This means that companies must constantly update their machine learning models with further information for continuing to use machine learning in Banking to prevent fraud.

How Does Machine Learning Overcome The Traditional Security Techniques Used By Banks?

Machine learning pushes the boundaries of what can be done with security. A traditional security strategy is to make the system as difficult to access as possible, stopping the bad guys before they get in. Banks often use biometrics and key cards to access their accounts, which are more challenging to hack than a username/password combination. 

But machine learning in banking prevents fraud even when it’s not done by someone trying to access an account. It can also be used to flag suspicious behavior so that humans can investigate it and decide whether or not it’s worth taking action on.

Machine learning algorithms can analyze data from all sources—customer transactions, social media posts, etc.—and find patterns that indicate fraudulent activity or other risks. These algorithms are trained on examples of fraud so that they know what to look for when new transactions occur.

What Are The Benefits of Machine Learning In Fraud Detection?

Machine learning has been the buzzword in the tech industry for some time. From self-driving cars to automated customer engagement, machine learning is everywhere.

But what does it mean? Let’s look at some of the benefits of using machine learning in Banking to prevent fraud.

  • Speed

Machine learning can help improve the speed of fraud detection by reducing the time it takes to detect and flag suspicious activity. Machine learning algorithms can be trained to automatically flag transactions with a high risk of fraud. This can significantly improve your ability to identify fraudulent transactions quickly so you can act on them before they become too costly to remediate.

  • Efficiency 

Machine learning also improves efficiency by automating many manual tasks that waste time and effort. For example, machine learning in banking to prevent fraud can identify known bad actors who are likely to commit fraud in the future, so you can block their access to your business immediately without having to review every transaction they make manually. 

  • Scalability 

Machine learning allows you to scale up or down your fraud detection capabilities as needed. This is important because fraud patterns change over time as criminals adapt their approach or new types of fraud emerge. Machine learning algorithms are designed with built-in flexibility to adapt quickly when new threats emerge or old threats change tactics. 

  • Accuracy 

Finally, machine learning offers increased accuracy over traditional methods because it uses data from all available sources—including humans—to learn what normal behavior looks like and spot anomalies that indicate potential problems.

What Are Some Of The Ways Machine Learning Can Be Used To Detect And Block Fraud?

There are many different techniques to detect and block suspicious cases. Some of them include the following – 

  • Classification

Classification assigns a label to an observation based on a set of observed values used as predictors. The predictors are inserted into the algorithms, which use training data to learn what labels to give. These predictions can then be used for fraud detection. This is done by identifying fraudulent transactions or users by classifying them as fraudulent or not fraudulent.

  • Regression

Regression is a supervised learning method that predicts future outcomes based on historical data. The regression algorithms can be used in fraud detection to predict the likelihood that a transaction will be fraudulent based on historical data about previous transactions that were labeled as fraudulent or not fraudulent by humans.

  • Clustering And Anomaly Detection

Clustering and anomaly detection are unsupervised learning methods that can be used for fraud detection by identifying patterns within your data that suggest fraud may occur, such as many small withdrawals from an account or many large purchases made at one store over time.

  • Anomaly Detection

Machine learning algorithms search for patterns in existing data that are not typical of what you would expect. If a new transaction is entered into your system and doesn’t fit the pattern of existing transactions, it could be an anomaly.

  • Decision Trees

A decision tree is a tree-like diagram that shows all possible paths that can take place in a decision process. A decision tree algorithm takes in data and tests each piece of information against all possible outcomes to determine if they’re true or false. If any single piece of information leads to an inaccurate result, the entire transaction is flagged as fraudulent.

  • Neural Networks

Neural networks are used to detect fraud in several ways. They can be trained to recognize patterns that indicate fraudulent transactions, such as repeated requests for withdrawals from an ATM or many purchases at one store within a short period. 

Neural networks can also monitor customer behavior over time and flag suspicious activities like sudden changes in spending habits or changes in the type of purchases being made (from low-risk items like groceries to high-risk items like jewelry).

  • Natural Language Processing (NLP)

NLP refers to technologies that use machine learning algorithms to analyze text data and extract meaningful information. 

For example, NLP software might analyze customer statements and detect instances where someone has been using their bank account number on multiple credit card applications without having applied for those cards themselves. This could indicate that they have been victims of identity theft or another fraud scheme.

Summing It Up

If you’re looking to implement machine learning in banking to prevent fraud or other systems, Mindfire Solutions has got you covered. Our goal is to take the guesswork out of it and ensure you get the most out of your investment.

We have the experience and expertise to help you implement machine-learning algorithms for your security and other needs. Our team deeply understands this technology’s potential, and we can work with you to determine the best way to use it in your organization. Contact us today to see how we can help!


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AI in Healthcare

AI in Healthcare Insurance

AI has transformed industries and has immense potential to reshape the healthcare landscape. Medical data like patient visits, prescriptions contain valuable information which can be extracted to create a more structured data format. This has become possible with Artificial Intelligence and Machine Learning. This in turn offers insights into the trends and facilitates collaboration between different healthcare units. Applications for AI in healthcare range from early detection of symptoms to potential diagnosis at a fraction of the cost and increased accuracy.

What is Optical Character Recognition?

Artificial intelligence refers to the use of Machine Learning algorithms to mimic human behavior. Say we want to create a program that takes an input and gives an output. The program uses some decision-making mechanism to arrive at an output. This is primitively done by something called IF-ELSE statements. The limitation of these statements is that one needs to know the exact condition that will produce the desired output. For example, we know the marks of a student and we want to assign a grade, we can hardcode the same using if-else statements – IF marks > 90 then grade = ‘A’. In this case, we have a clearly defined boundary that helps us arrive at our output. But what happens when we don’t have a clearly defined boundary?

Let me explain this with an example. Suppose we have an image of a letter. And we want our program to tell us which letter it is. What kind of code shall we write? This is an example of a problem that doesn’t have a clearly defined boundary. One way to solve this problem is to build a model, which does not use if-else statements but works quite differently. The idea is to feed the program an input image (say an image of ‘A’) and tell the program that it’s an ‘A’. We do this repetitively and try to get the program to learn what ‘A’ looks like. Hence, the term “Machine Learning”. We do the same for every letter or number. This falls under the computer vision category, namely Optical Character Recognition.

Overview of Natural Language Processing :

Natural language Processing is an area of AI and Linguistics and it deals with the application of computational techniques and models to the analysis of human languages. NLP deals with processing written text including but not limited to extracting useful information such as named entities, etc. This requires an understanding of what a word means, which is not something computers generally have. This too is done by learning, where a huge amount of text is fed to the model, and it will try to learn patterns and dependencies between words. In the next section, we will define a problem statement and see how an application of these two concepts can help us make better decisions faster, way faster.

Problems faced by the Healthcare Industry :

Insurance companies have certain parameters to account for when finalizing the premiums they charge for their insurance. One of the most important parameters is the medical history of the member/ insurance holder. The idea is that if a member has more cases of medical history then that member is more likely to raise a claim in the future, thereby costing more.

So the insurance companies need to know relevant details of the medical history and assign risk scores (this could be a topic for another article) corresponding to the medical history. But medical histories are generally stored in paper form. Suppose you had a doctor’s visit, everything there is generally stored in paper format. One might have had any number of tests, and these are in most cases available in paper form. Thus, going through all the paperwork of a member’s medical history is an engaging task. This might take a person anywhere between 30mins – 1 hour. So, is there a way to automate the task?

How can AI help to resolve the issue?

The first step in the process is to get the images of all our documents. The idea is to use OCR to extract the text information from images and subsequently use NLP to extract relevant information, such as diagnosis, procedure or operation, medications that the member is on, etc. There are many open-source OCR engines, one such engine is Tesseract OCR.

Tesseract is used for text detection within large documents or used in concurrence with an external text detector. It can be used directly or through an API to extract text from images. It supports Unicode and has the ability to discern more than 100 languages and can also be trained to recognize multiple languages. The current model, Tesseract 4 focuses on line recognition. It also supports the legacy Tesseract 3 in recognizing character patterns. It is compatible with many programming languages and frameworks. CNN (Convolutional Neural network) is typically used for single character whereas RNN is used for multi-characters.

The next step is to feed the image to the tesseract engine. It will give a file output that contains text extracted from the image. There are various Open source NLP engines. We then feed lines from the text file to our NLP engine which will read each line and use dependencies between words to figure out what our diagnosis, procedure or medications will be. Most NLP engines offer phrase matching capabilities, so we can look for particular phrases as well. We can narrow our search to a specific diagnosis (or any other information of relevance) as well.

Final Steps :

Once we have extracted output from the NLP engine, we can create a database that will have fields such as member name, document type, extracted information, etc. We can use the above pipeline and feed it numerous files at once. After every output, we can append the same to our database.

This process takes a mere few seconds to go through all the files of a member. Using this, we will be able to read through the medical history of thousands of members in a matter of hours.

Like other businesses, if you too are looking to develop applications for AI in healthcare, Mindfire Solutions can be your partner of choice. We have deep expertise in AI and ML Capabilities. With a team of highly skilled and certified software professionals, that have developed many custom solutions for our global clients over the years.

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