As the finance sector operates on tons of personal data and billions of critical transactions every second, it becomes especially vulnerable to fraudulent activities. Scammers are always seeking to crack the servers to get valuable data for blackmailing. 

According to PwC’s Global Economic Crime and Fraud Survey 2020, respondents reported losses of a whopping $42 billion over the past 24 months due to fraudulent activities. Banks and financial institutions have no choice but to tighten their defences by adopting innovative technologies such as machine learning. 

Machine learning is providing the financial services sector with the means to protect their businesses and defeat cybercriminals. According to a report by Markets and Markets, the global fraud detection and prevention (FDP) market is expected to grow to USD 38.2 billion from USD 20.9 by 2025, at a CAGR of 12.8%.

Fraud detection and prevention areas include money laundering, insurance claims, electronic payments, and bank transactions. 

In this article, we will discover the main ways machine learning in finance can be used for fraud detection. Read on!

Major areas of financial fraud in the Finance sector

Before we dive into the details of machine learning in financial industry, let’s get to the different areas where financial fraud detection software can assist enterprises. 

1. Email phishing: This is a type of cybercrime wherein attackers send fake messages and website links to users via email. These emails are seemingly legit and authentic that anyone can misjudge them and enter the vulnerable data that puts them at risk. 

To avoid such situations, you can use automated methods for detecting phishing using machine learning. These methods are based on classical Machine Learning algorithms for classification and regression.

2. Credit card fraud: In an increasingly digital world, credit card fraud has become quite common. This type of financial fraud involves stealing debit cards or credit card numbers through unsecured internet connections. 

Machine learning algorithms help identify which actions are authentic and which ones are illegal. If someone tries to cheat the system, an ML model can alert the bank and take measures to negate the activity. 

3. Mobile fraud: Machine learning integration in anti-fraud systems is particularly crucial when payment methods extend beyond physical cards and into the realm of mobile phones. 

Smartphones now feature NFC chips, enabling users to pay for products just with their phones. This means your smartphone is prone to hacking and cyber threats. Machine learning in Finance is an effective tool to detect abnormal activities for each user, thus minimizing mobile fraud risks.

4. Identity theft: Information such as user’s name, bank details, passwords, login credentials, and other extremely sensitive information is under great threat if a cybercriminal comes into play. Identity theft puts both individuals and enterprises at risk. 

Machine learning in Finance helps examine and check identity documents such as passports or driving licenses against secure databases in real-time to ensure all fraud cases are detected. Besides, ML can be also used for fighting fake IDs by enabling biometric scanning and face recognition.

5. Insurance claims: Insurance fraud typically includes fake claims of car damage, property, and even unemployment. To reduce such frauds, insurance companies spend an extensive amount of time and resources to validate each claim. However, this process is expensive as well as prone to hacking. 

Machine learning due to its superior pattern recognition capabilities helps resolve insurance claims with utmost accuracy and find fake claims. 

[Also Read: 5 Ways Fintech Industry is Using AI to Woo Millennials]

Why should you use machine learning for fraud prevention?

As evident from the ML uses in different financial fraud areas mentioned above, the finance and banking industry considers machine learning to be extremely helpful in financial fraud detection. The high volume of transactional and consumer data makes it ideal for the application of complex machine learning algorithms. ML helps banks and financial institutions to identify and flag fraudulent activity in real-time.

The increased accuracy of machine learning algorithms provides financial firms with a significant reduction in the number of false positives (where transactions are incorrectly flagged as declined and fraudulent) and false negatives (where genuine instances of fraud are missed). This is the reason why ML has taken the prime spot in the finance sector. 

If still in doubt on how machine learning can help in detecting frauds for your financial services business, go through the pool of benefits mentioned below.

1. Faster data collection: As the velocity of commerce is increasing, it’s important to have quicker solutions like machine learning to detect fraud. Machine learning algorithms can evaluate enormous amounts of data in a very short amount of time. They have the ability to continuously collect and analyze data in real-time and detect frauds in no time.

2. Effortless scaling: Machine learning models and algorithms become more effective with increasing data sets. Machine’s learning improves with more data because the ML model can pick out the similarities and differences between multiple behaviors. 

Once genuine and fraudulent transactions are found, the system can work through them and begin to pick out those that fit the bucket. 

3. Increased efficiency: Contrary to humans, machines can perform repetitive tasks and detect changes across large volumes of data. This is critical for fraud detection in a much shorter amount of time.

Algorithms can accurately analyze hundreds of thousands of payments per second. This reduces costs as well as time taken to analyze transactions, thus making the process more efficient. 

4. Reduced security breach cases: With the implementation of machine learning systems, financial institutions can combat frauds and provide the highest level of security to their customers. It works by comparing every new transaction with the previous (personal information, data, IP address, location, etc) one and detecting suspicious cases. As a result, financial units can prevent fraud related to payment or credit cards.

Now that we have seen the benefits of using machine learning, let’s dive into the machine learning models used for fraud detection.

Machine learning models and algorithms for fraud detection

Here are the types of machine learning models and algorithms used for detecting financial fraud in the finance industry. So, let’s understand them one by one. 

1. Supervised learning: Supervised learning works for cases like fraud detection in deep learning environments in FinTech. In this model, all information has to be labeled as good or bad. And it is based on predictive data analysis. 

2. Unsupervised learning: An unsupervised learning model detects anomalous behavior in cases where there is no such data or little transaction data available. It continuously analyzes and processes new data and updates its models based on the findings. It learns the patterns over time and decides whether they’re legitimate or fraudulent operations.

3. Semi-supervised learning: It works for cases where labeling information is either impossible or too expensive and requires human intervention. 

4. Reinforcement learning: This model allows machines to detect ideal behavior automatically within a specified context. It helps machines to learn from the environment and find actions that minimize risks.

How does an ML system work for fraud detection?

The fraud detection process starts with gathering and segmenting the data using machine learning systems. Then the machine learning model is fed with training algorithms to predict the probability of fraud.

Below are the steps that show how an ML system works for fraud detection:

1. Input data: To detect fraud, the machine learning system first needs to collect data. The more data an ML model gets, the better it can learn and polish its fraud detection skills.

2. Extract features: The next step is feature extraction. At this stage, features describing both good and fraudulent customer behaviors are added. These features usually include:

• Identity: It comprises the fraud rate of customers’ IP addresses, age of their account, number of devices they were seen on, etc.
• Order: This feature shows the number of orders customers made, average order value, number of failed transactions, and more.
• Location: This feature helps to find out if the shipping address matches the billing address, shipping country matches the country of the customer’s IP address, and fraud rate at the customer’s location. 
• Payment methods: It helps to identify the fraud rates in credit/debit cards issuing banks, the similarity between customer name and billing name, etc. 
• Network: It includes the number of emails, phone numbers, or payment methods shared within a network.

3. Train algorithm: An algorithm is a set of rules that needs to be followed by an ML model to decide whether an operation is fraudulent or legitimate. The more data your business can provide for a training set, the better the ML model will be.

4. Create a model: When the training is over, your company will receive a machine learning model for fraud detection. This model can detect fraud in no time with high accuracy. But to be effective in fraud detection, a machine learning model needs to be constantly improved and updated.

Financial fraud detection using machine learning – Use cases

Using ML-powered modern fraud protection systems, financial institutions greatly reduce the risks of missing suspicious transactions, human errors, and cases of security breaches. Machine learning algorithms can process huge volumes of data and protect it from fraud. 

Compliance.ai is a startup that uses adaptive machine learning models in FinTech to automate research and track financial regulatory content and regulatory updates in a single platform.

Companies like PayPal are also using machine learning to enhance their fraud detection and risk management capabilities. Through a combination of linear, neural networks, and deep learning techniques, PayPal’s risk management engines can determine the risk levels associated with a customer within milliseconds.

Machine learning helped such large corporations to prevent issues such as fake accounts, payment frauds, and suspicious transactions. ML takes care of all the dirty work of predictive analytics and data analysis and allows companies to grow safe from fraud. 

Final thoughts

Enterprises all over the world have started using machine learning to prevent financial fraud. It is the most innovative tool that can help prevent fraudulent operations that lead to greater losses every year. 

Machine learning allows creating more complex algorithms for analyzing various transactions and suspicious financial behavior, thus minimizing the risks of financial loss. 

That’s why hiring machine learning development services has become the topmost priority for the financial services  industry. At Appinventiv, our experts empower different companies with various innovative solutions and approaches to improve their productivity and reduce financial risk. 

If you want to implement ML systems or financial fraud detection software into your business, feel free to contact us. We will help you understand the immense benefits of this innovative technology and help you scale your business while reducing overall costs. 

The post How Machine Learning Helps in Financial Fraud Detection in the FinTech Industry appeared first on Appinventiv.

“Signature-based malware detection is dead. Machine learning based Artificial Intelligence is the most potent defence the next-gen adversary and the mutating hash.”
― James Scott, Senior Fellow, Institute for Critical Infrastructure Technology

The time of generic services and simpler technologies is long gone and today we’re living in a highly machine-driven world. Machines which are capable of learning our behaviors and making our daily lives easier than we ever imagined possible.

If we go deeper into this thought, we’ll realize, how sophisticated a technology has to be for learning on its own any behavioral patterns that we subconsciously follow. These are not simple machines, these are more than advanced.

Technological realm today is fast-paced enough to quickly switch between Brands and Apps and technologies if one happens to not fulfill their needs in the first five minutes of them using it. This is also a reflection upon the competition this fast pace has led to. Mobile app development companies simply cannot afford to be left behind in the race of forever evolving technologies.

Today, if we see, there is machine learning incorporated in almost every mobile application we decide to use. For instance, our food delivery app will show us the restaurants which deliver the kind of food we like to order, our on-demand taxi applications show us the real-time location of our rides, time management applications tell us what is the most suitable time for to complete a task and how to prioritize our work. The need of worrying over simple, even complicated things is ceasing to exist because our mobile applications and our smartphone devices are doing that for us.

Looking at the stats, they will show us that

• AI and Machine Learning-driven apps is a leading category among funded startups
• Number of businesses investing in ML is expected to double over the next three years
• 40% of US companies use ML to improve sales and marketing
• 76% of US companies have exceeded their sales targets because of ML
• European banks have increased product sales by 10% and lower churn rates by 20% with ML

The idea behind any kind of business is to make profits and that can only be done when they gain new users and retain their old users. It might be a bizarre thought for mobile app developers but it is as true as it can be that Machine learning app development has the potential of turning your simple mobile apps into gold mines. Let us see how:

How Machine Learning Can Be Advantageous For Mobile App Development?

• Personalisation: Any machine learning algorithm attached to your simpleton mobile application can analyze various sources of information from social media activities to credit ratings and provide recommendations to every user device. Machine learning web app, as well as mobile app development, can be used to learn.

1. Who are your customers?
2. What do they like?
3. What can they afford?
4. What words they’re using to talk about different products?

Based on all of this information, you can classify your customer behaviors and use that classification for target marketing. To put simply, ML will allow you to provide your customers and potential customers with more relevant and enticing content and put up an impression that your mobile app technologies with AI are customized especially for them.

To look at a few examples of big brands using machine learning app development to their benefits,

1. Taco Bell as a TacBot that takes orders, answers questions and recommends menu items based on your preferences.
2. Uber uses ML to provide an estimated time of arrival and cost to its users.
3. ImprompDo is a Time management app that employs ML to find a suitable time for you to complete your tasks and to prioritise your to-do list
4. Migraine Buddy is a great healthcare app which adopts ML to forecast the possibility of a headache and recommends ways to prevent it.
5. Optimise fitness is a sports app which incorporates an available sensor and genetic data to customise a highly individual workout program.

• Advanced Search: Machine learning app ideas let you optimize search options in your mobile applications. ML makes the search results more intuitive and contextual for its users. ML algorithms learn from the different queries put by customers and prioritize the results based on those queries. In fact, not only search algorithms, modern mobile applications allow you to gather all the user data including search histories and typical actions. This data can be used along with the behavioural data and search requests to rank your products and services and show the best applicable outcomes.

Upgrades, such as voice search or gestural search can be incorporated for a better performing application.

• Predicting User Behavior: The biggest advantage of machine learning app development for marketers is that they get an understanding of users’ preferences and behavior pattern by inspection of different kind of data concerning the age, gender, location, search histories, app usage frequency, etc. This data is the key to improving the effectiveness of your application and marketing efforts.

Amazon’s suggestion mechanism and Netflix’s recommendation works on the same principle that ML aids in creating customized recommendations for each individual.

And not only Amazon and Netflix but mobile apps such as Youbox, JJ food service and Qloo entertainment adopt ML to predict the user preferences and build the user profile according to that.

• More Relevant Ads: Many industry experts have exerted on this point that the only way to move forward in this never-ending consumer market can be achieved by personalizing every experience for every customer.

“Most analogue marketing hits the wrong people or the right people at the wrong time. Digital is more efficient and more impactful because it can hit only the right people, and only at the right time.” – Simon Silvester, Executive Vice President Head of Planning at Y&R EMEA

According to a report by The Relevancy group, 38% of executives are already using machine learning for mobile apps as a part of their Data Management Platform (DMP) for advertising.

With the help of integrating machine learning in mobile apps, you can avoid debilitating your customers by approaching them with products and services that they have no interest in. Rather you can concentrate all your energy towards generating ads that cater to each user’s unique fancies and whims.

Mobile app development companies today can easily consolidate data from ML that will in return save the time and money went into inappropriate advertising and improve the brand reputation of any company.

For instance, Coca-Cola is known for customizing its ads as per the demographic. It does so by having information about what situations prompt customers to talk about the brand and has, hence, defined the best way to serve advertisements.

• Improved Security Level: Besides making a very effective marketing tool, machine learning for mobile apps can also streamline and secure app authentication. Features such as Image recognition or Audio recognition makes it possible for users to set up their biometric data as a security authentication step in their mobile devices. ML also aids you in establishing access rights for your customers as well.

Apps such as ZoOm Login and BioID use machine learning for mobile apps to allow users to use their fingerprints and Face IDs to set up security locks to various websites and apps. In fact, BioID even offers a periocular eye recognition for partially visible faces.

ML even prevents malicious traffic and data from reaching your mobile device. Machine Learning applications algorithms detect and ban suspicious activities.

How are developers using the Power of Artificial intelligence In Mobile Application development?

After learning that what is machine learning app, let us take a look at the advantages of AI powered mobile apps which are never-ending for Users as well as for mobile app developers. One of the most sustainable uses for developers is that they can create hyper-realistic apps using Artificial Intelligence.

The best usages can be:

• Machine learning can be incorporated as a part of Artificial Intelligence in mobile technology.
• It can be used for predictive analysis which is basically the processing of large volumes of data for predictions of human behaviour.
• Machine learning for mobile apps can also be used for assimilating security and filtering out harmful data.

Machine learning empowers an optical character recognition (OCR) application to identify and remember the characters which might have been skipped from the developer’s end.

The concept of machine learning also stands true for Natural Language Processing (NLP) apps. So besides reducing the development time and efforts, the combination of AI and Quality Assurance also reduces the update and testing time phases.

What Are The Challenges with Machine Learning and their solutions?

Like any other technology, there is always a series of challenges attached to machine learning as well. The basic working principle behind machine learning is the availability of enough resource data as a training sample. And as a benchmark of learning, the size of training sample data should be large enough so as to ensure a fundamental perfection in machine learning algorithm.

In order to avoid the risks of misinterpretation of visual cues or any other digital information by the machine or mobile application, following are the various methods which can be used:

• Hard Sample mining – When a subject consists of several objects similar to the main object, the machine is ought to confuse between those objects if the sample size provided for analysis as the example if not big enough. Differentiating between different objects with the help of multiple examples is how the machine learns to analyse which object is the central object.
• Data Augmentation – When there is an image in question in which the machine or mobile application is required to identify a central image, there should be modifications made to the entire image keeping the subject unchanged, thereby enabling the app to register the main object in a variety of environments.
• Data addition imitation – In this method, some of the data is nullified keeping only the information about the central object. This is done so that the machine memory only contains the data regarding the main subject image and not about the surrounding objects.

Which are the Best Platforms for the development of a mobile application with Machine Learning?

• Azure – Azure is a Microsoft cloud solution. Azure has a very large support community, and high-quality multilingual documents, and a high number of accessible tutorials. The programming languages of this platform are R and Python. Because of an advanced analytical mechanism, the developers can create mobile applications with accurate forecasting capabilities.
• IBM Watson – The main characteristic of using IBM Watson, is that it allows the developers to process user requests comprehensively regardless of the format. Any kind of data. Including voice notes, images or printed formats is analyzed quickly with the help of multiple approaches. This search method is not provided by any other platform than IBM Watson. Other platforms involve complex logical chains of ANN for search properties. The multitasking in IBM Watson places an upper hand in the majority of the cases since it determines the factor of minimum risk.

• Tensorflow – Google’s open-source library, Tensor, allows developers to create multiple solutions depending upon deep machine learning which is deemed necessary to solve nonlinear problems. Tensorflow applications work by using the communication experience with users in their environment and gradually finding correct answers as per the requests by users. Although, this open library is not the best choice for beginners.

• Api.ai – It is a platform that is created by the Google development team which is known to use contextual dependencies. This platform can be very successfully used to create AI based virtual assistants for Android and iOS. The two fundamental concepts that Api.ai depends on are – Entities and Roles. Entities are are the central objects (discussed before) and Roles are accompanying objects that determine the central object’s activity. Furthermore, the creators of Api.ai have created a highly powerful database that strengthened their algorithms.

• Wit.ai – Api.ai and Wit.ai have largely similar platforms. Another prominent characteristic of Wit.ai is that it converts speech files into printed texts. Wit.ai also enables a “history” features which can analyze context-sensitive data and therefore, can generate highly accurate answers to user requests and this is especially the case of chatbots for commercial websites. This is a good platform for the creation of Windows, iOS or Android mobile applications with machine learning.

Some of the most popular apps such as Netflix, Tinder, Snapchat, Google maps and Dango are using AI technology in mobile apps and machine learning business applications for giving their users a highly customised and personalised experience.

Machine learning to benefit mobile apps is the way to go today because it loads your mobile app with enough personalization options to make it more usable, efficient and effective. Having a great concept and UI is one pole of the magnet but incorporating machine learning is going a step forward to provide your users with the best experiences.

[Also Read: Estimate the Time, Cost, and Deliverables of an ML App Project]

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