The digital transformation in the financial industry has revolutionized the way we manage our money. From mobile banking to online investing, it has made financial services faster, easier, and more accessible than ever. However, with all this convenience comes a new challenge: FRAUD. Do you know fraudsters are constantly coming up with new tricks to steal money?
In fact, the global fraud detection and prevention market is projected to grow from $52.82 billion in 2024 to $255.39 billion by 2032 at a CAGR of 21.8%. That is why fintech companies need to invest in data analytics consulting to preserve their data and customers’ trust. But how does it work? In this blog, we are going to tell you how fintech companies can identify patterns and red flag activities using data analytics. You will also learn about future technologies that will help you fight fraud. But first, let’s understand the basics:
What is Data Analytics in Fintech?
Fintech companies use statistical, computational, and analytical tools to process and interpret financial data. It aims to uncover patterns, trends, and insights to inform decision-making and optimize financial services. You can also use data analytics for the following:
- Risk management
- Fraud detection
- Customer segmentation
- Personalized financial advice
- Algorithmic trading
With the use of advanced data analytics, you can enhance your services, improve efficiency, and offer innovative solutions. Data analytics helps in predicting market movements, assessing creditworthiness, and automating processes. As a result, it ultimately drives the digital transformation of financial services.
What Information Do Fintech Companies Use?
When you install fintech apps, you permit them to fetch details from your smartphone. Fintech companies collect a wide range of data about their users, including:
- Transaction Data: This includes the transfer amount, time and location of the transaction, and the device used.
- Customer Data: The service providers also collect customers’ names, addresses, phone numbers, and email addresses.
- Behavioral Data: They also have information like how often you log in to your account, what features you use, and how you navigate the app, etc.
Apply AI Data Analytics Techniques to Fight Fraud
After collecting the necessary information, fintech companies use it to train the algorithm. Generally, fintech companies use a variety of data analytic programs to identify fraud, such as:
Machine Learning (ML)
Imagine a super-smart computer program that learns from experience. That’s machine learning! ML algorithms analyze historical data to identify patterns associated with fraudulent activities. They then use these patterns to predict future fraud attempts. For example, an ML algorithm can easily detect a red alert when it finds transactions from a new device in a different country.
Statistical Analysis
Machine learning can analyze your data, and if the algorithms find unusual high-value transactions from a location you have never been to, that would be a red flag. You will often get a notification against the transactions. In this way, business intelligence services ensure safe and secure transactions for your customers.
Network Analysis
The data analytics solutions also identify connections between different data points. For example, if multiple accounts with suspicious activity seem to be linked to the same email address or IP address, that could be a sign of an organized fraud ring.
However, biased information may not work for your business, so you need expert assistance to train the algorithm in a proper way that matches your business needs.
How Do You Detect Fraudsters with Data Analytics?
Now it’s time for some real-world examples of how these data analytics solutions help your business. Here are some specific examples of how data analytics can be used to fight fraud:
Identifying Fake Accounts
ML algorithms can analyze inconsistencies in user data (e.g., a birthdate that does not match the reported age) to identify fake accounts created for fraudulent purposes.
Catching Unauthorized Transactions
Data analytics can monitor transaction patterns and flag suspicious activity, such as a large purchase made from a location far from one’s usual spending habits.
Preventing Account Takeovers
Data analytic programs analyze login attempts to identify unusual activity, such as logins from a new device or location. This helps prevent fraudsters from taking over your account.
These are a few common examples, but there are lots of ways to detect fraud and ensure a better customer experience.
The Benefits of Using Data Analytics for Fraud Prevention
A study by Juniper Research found that AI will save banks $900 million in operational costs by 2028 and save 29 million digital onboarding hours. That’s not all! AI & ML also help in fraud prevention, which builds trust and reliability in the industry. There are many advantages of using data analytics services for fraud prevention, especially in the fintech business, such as:
- Faster Detection: Data analytics software can identify fraud in real time, which helps minimize the damage.
- Improved Accuracy: AI data analytics is more accurate at detecting fraud than traditional methods that rely on simple rules.
- Reduced Costs: Yes, with the help of business intelligence services, you can prevent fraud and save money that would have otherwise been lost to fraudsters.
- Enhanced Customer Experience: With their advanced safety/preventive features, data analytics build trust with customers and encourage them to use fintech services confidently.
Challenges and Considerations of Using Data Analytics for Fraud Detection
Now, you understand that data analytics is a powerful tool, but you also consider the following challenges before investing money in developing data analytics solutions for your business:
Data Privacy
Fintech companies need to ensure they are collecting and using customer data responsibly and in accordance with data privacy regulations.
Algorithmic Bias
Data analytics models can be biased if the data they are trained on is biased. This can lead to false positives (flagging legitimate transactions as fraudulent) or false negatives (missing fraudulent transactions). That is why you need data analytics consulting experts to make your algorithms unbiased, customized, and suitable for your business.
Evolving Fraudsters
Fraudsters are constantly adapting their tactics, so data analytics models need to be continuously updated to stay ahead of the curve.
The Future of Data Analytics in Fintech Fraud Prevention
The latest advancement in data analytics offers a powerful shield against fraudulent activities. Now, you will learn some of the most promising future technologies that will revolutionize data analytics in Fintech fraud prevention:
Artificial Intelligence (AI) & Machine Learning (ML)
AI and ML are already making waves in fraud detection. These technologies can analyze vast amounts of data, including transaction history, customer behavior, and device fingerprints, to identify patterns. In the future, you can expect to see:
- Self-learning AI Models
The new generation of AI will continuously learn and adapt to new fraud tactics, always staying a step ahead of criminals.
- Unsupervised Anomaly Detection
These AI systems will be able to detect unusual patterns without prior training. It uncovers even the most novel fraudulent schemes.
Graph Analytics
Traditional fraud detection often treats data points in isolation. Graph analytics connects data points to form a network. As a result, it reveals hidden relationships and uncovers complex fraud rings. Imagine a graph where accounts, transactions, and devices are all connected. AI can identify suspicious clusters that might indicate coordinated fraudulent activity.
Big Data Integration and Cloud Computing
Cloud computing provides the scalability and processing power needed to handle such a large amount of data. Moreover, integrating data from various sources, including social media and public records, will give a more holistic view of customer behavior and identify potential fraudsters.
Behavioral Biometrics
This emerging technology analyzes a user’s behavioral patterns during interactions, such as typing speed, mouse movements, and even facial expressions. Deviations from a user’s established baseline behavior may indicate an account takeover or other fraudulent activities. As wearables and other biometric sensors become more prevalent, you can expect to see them incorporated into fraud detection systems.
Explainable AI (XAI)
While AI is powerful, its decision-making process can sometimes be a black box. XAI aims to make AI models more transparent, allowing humans to understand why a particular transaction was flagged as fraudulent. This transparency is crucial for building trusted AI-powered fraud detection systems for fintech companies.
Conclusion
The future of data analytics in fintech fraud prevention is bright. However, business intelligence services will play a vital role in developing cutting-edge technologies and collaborating with other financial institutions for a safer and more secure environment. As these technologies evolve, you can expect more proactive fraud detection for fintech companies.
