Machine Learning is one of the most trending things in the current tech world. A number of businesses, from e-commerce to banking & finance app development solutions, are looking to hire ML developers from top companies who can develop amazing ML apps for their business.
According to builtwith.com, 45% of technology companies prefer to use AI and Machine Learning for their ongoing projects. One of the most widely discussed applications is those of Machine Learning in the finance and banking industry.
So how can applications of Machine Learning in the banking industry shape our future? How does fraud detection become easier with Machine Learning?
In this blog, we will discuss the methods for fraud detection using Machine Learning, along with other applications of Machine Learning in finance and banking. Before this, you must be aware of the Machine Learning basics and fraud detection.
What is Fraud Detection?
It is the process of identifying fraudulent activity. It is usually done by businesses to protect themselves from financial losses. Fraud detection can be done manually or using automated systems.
The total amount of losses experienced by businesses due to payment fraud has more than tripled in just the last few years. In 2011, $10 billion was lost due to illicit payments. In 2020, this figure rose to more than $32 billion, with 6.83 cents out of every $100 being lost to fraud.
And according to industry analysts, the costs of payment fraud will only continue to increase. It is predicted that fraud losses will grow a further 25% and surpass $40 billion by 2027. (SDK finance)
There are two types of fraud detection:
Preventive: This type of fraud detection identifies potential fraudulent activity before it happens and takes measures to prevent it from happening.
Detective: This type of fraud detection identifies fraudulent activity after it has already taken place.
What is a machine learning app and how it works?
When it comes to Machine Learning concepts, as the name suggests, it is the power of machines to learn and improvise things accordingly. A Machine Learning app learns from its own experiences without being explicitly programmed. These apps can access information and use this data to learn & improve themselves.
Several industries also use ML for operations such as identifying unwanted emails, providing an adequate recommendation of the product to customers, and offering an accurate medical diagnosis. For instance, Coca-Cola is using Machine Learning for product development. Using the data they collected from various dispensaries of soda sources, they were able to tell what flavor was preferred by maximum people. This is what helped them launch the ‘Cherry Sprite’ in the nation.
Here is another example of how ML applications are used to mitigate fraud. Huawei Technologies is using the analytical database for fraud detection with Machine Learning in real-time. They are using an automatic learning model that analyzes approved or rejected transactions. It is easy for the system to discover transactions that are fraudulent using this data.
So, a machine learning app is instrumental in fraud detection for any business, including banking. The advent of machine learning and artificial intelligence has made a lot easier to detect fraud in businesses today. Online money transactions are secure now and risk-free.
So, a Machine Learning app is instrumental in fraud detection for any business, including banking. The advent of Machine Learning and artificial intelligence has made it a lot easier to detect fraud in businesses today. Online money transactions are secure now and risk-free.
If you are thinking of including ML in your banking and finance industry, get connected with the Machine Learning developers working in one of the best Machine Learning Development Companies, this will help you make efficient use of it in fraud detection.
Read More: Why is Machine Learning the best for Fraud Prevention
Machine Learning Algorithms Used In Fraud Detection
Fraud detection in the banking and finance industry is a preventive measure to stop potential fraudulent activities from happening. Banks use Machine Learning algorithms to automatically detect fraudulent activities such as money laundering, credit card fraud, and identity theft.
There are three main methods for fraud detection using Machine Learning:
Supervised Learning: In this method, the data is labeled as either fraud or not fraud. The machine-learning algorithm then learns from this data and is able to predict whether new data is fraudulent or not.
Unsupervised Learning: In this method, the Machine Learning algorithm looks for patterns in the data. If it finds a pattern that is not normally seen, it will flag it as potentially fraudulent.
Anomaly Detection: In this method, the Machine Learning algorithm looks for outliers in the data. An outlier is a data point that is far from the rest of the data. This could be an indication of fraudulent activity.
The process of fraud detection using machine learning is explained below:
The process starts with collecting and segmenting the data. After this, the Machine Learning model is fed with training sets in order to predict the fraud probability. It is a 3-step process explained below:
First Step: Extracting Data
The extracted data will be divided into three different segments: training, testing, and cross-validation. The algorithm will be trained in a partial set of data and adjust parameters in a test set. The performance of the data is measured using the cross-validation set. High-performance models will be tested for several random divisions of data to ensure consistency in the results.
Second step: Providing Training Sets
Prediction is the main application of Machine Learning that is used in fraud detection. The data used to train the ML models consist of records with the two output values for several input values. Records are often obtained from historical data.
Third step: Building Models
Model building is an essential step in fraud detection or anomaly in data sets. First, determine how to make that prediction based on previous examples of input and output data. Now, you can further divide the prediction problem into two types of tasks:
Moreover, if you are thinking of developing a Machine Learning application, avail of Machine Learning development services from the best Machine Learning company; doing so, you will be able to develop multi-functional applications.
Benefits Of Using Machine Learning In Finance And Banking For Fraud Detection
1) Cost-Effective & easy to maintain
A Machine Learning app for banking can perform better when you enter a large amount of data. In systems that rely on rules, to maintain a fraud detection system, Finance & Mobile Banking Development companies have to spend a lot of money. But, when using Machine Learning in finance, things will be much easier and more profitable. The more data you are going to feed the systems will help the machines run more efficiently. Differentiating good and bad transactions become much simpler when you do this.
2) Fast verification
In a system that relies mainly on rules, things can get too complicated, and checking big data takes a lot of time. Merchants prefer to get their money faster and will be super happy when there is an implemented system that can verify huge volumes of data in just a few milliseconds. Fraud detection will be easy and simple when you choose this option. Real-time verification of a large number of transactions is only possible with Machine Learning application systems in finance.
3) Futuristic solution
When it comes to cybercriminals smart and use advanced tools & strategies to carry out their fraudulent activities, no matter how efficient your internal fraud team is, you will not find fraudulent transactions easily, as things will get more complicated.
Artificial intelligence and Machine Learning are the future, and, therefore, financial institutions and other industries must rely on Machine Learning applications in finance when it comes to preventing fraud. These systems can quickly learn the patterns and behavior of people who commit fraud and protect their organizations against such things.
Machines that receive the proper training will perform better than humans. They can do the repetitive work of data analysis with ease. The machines will scale all the cases that need human intervention promptly. Preventing fraudulent transactions from happening will be easy with the implementation of Machine Learning in finance because they will recognize non-intuitive and subtle patterns without any difficulty.
Algorithms in Machine Learning models become more effective with increasing data sets. While in rule-based models, the cost of maintaining a fraud detection system multiplies as the customer base increases!
Custom banking & finance software development services along with Machine Learning improve with more data because the ML model can detect the differences and similarities between multiple behaviors. Once they are informed which transactions are genuine and which are fraudulent, the systems of Machine Learning in finance can work through them and begin to select those that fit either of them.
They can also predict them in the future when dealing with new transactions. There is a risk on the scale at a rapid pace. If there is a fraud not detected in the training data device, Machine Learning will enable the system to ignore that type of fraud in the future.
Related: MACHINE LEARNING APP IDEAS 2021
There are many companies that are still wondering if it is worth investing in applications of machine learning in the finance & banking sector. If you’re an entrepreneur troubled by the same question, then the answer is a big yes! In fact, it will be a fruitful investment for businesses in 2019. This is especially true for business leaders looking to use machine learning in finance, banking, and other domains associated with the fintech industry.
Seeing all the benefits that an organization or institution will obtain and the money they will save in the future using banking mobile app solutions is incomprehensible. Therefore, it is time to use machine learning in finance to safeguard the money, customer data, and reputation of the brand.
Now, if you are curious to develop your next project using machine learning or any of its frameworks, then it is the right time to start with. You can also hire skilled ML web developers from a reliable software development company like ValueCoders.
FREQUENTLY ASKED QUESTIONS
Q)How Machine Learning in finance is used?
Machine Learning is used in finance for a variety of tasks, including identifying financial crimes such as money laundering, as well as for analyzing financial data to better understand market trends or predict customer behavior.
Q)What are the benefits of using Machine Learning in finance?
Ans) Machine Learning can be used to improve the accuracy of financial predictions and to automate tasks like – fraud detection or customer segmentation. Additionally, Machine Learning can help reduce the cost of financial services by automating manual processes.
Q)How will Machine Learning in finance develop in the future?
Machine Learning is constantly evolving, and the potential applications for Machine Learning in finance are ever-expanding. In the future, we can desire to see even more widespread use of Machine Learning across all aspects of financial services. Additionally, Machine Learning will continue to become more accessible to businesses of all sizes as the technology becomes more reasonable and easier to use.
Q) How can I get started with using Machine Learning in finance?
If you’re interested in using Machine Learning in finance, there are a few ways to get started. You can begin by exploring some of the open-source Machine Learning software that is available, such as TensorFlow. Alternatively, you can work with a Machine Learning development company, like ValueCoders, to build custom Machine Learning solutions for your business. Finally, you can also attend Machine Learning conferences or meetups to learn more about the technology and its potential applications in finance.