What is Behavioral Analytics? Everything You Need to Know

Almost twenty years ago, the first online payment took place and with the establishment of services Google Pay and PayPal, these online payments have become more simple, easy, and accessible for most consumers. This means that the behavior of customer has also changed, and the study of behavior analytics can be insightful for financial institutions at many levels which include reducing risks, improving customer experiences, and fight against fraudulent activities.

What are Behavioral Analytics?

Behavioral analytics revolves around the study of data, and this data is about how people in general and customers in specific act, what they do, and how and why they do what they do. Behavioral data science opens the door for us to find valuable patterns that can guide decisions in many areas of financial services.

9 User Behavior Analytics Examples

Here are some important user behavior analytics examples that matter when using behavioral analytics, especially for financial institutions.

1. Transaction Patterns: If a customer usually makes small purchases but suddenly tries to send a large amount of money, it might raise a red flag for possible fraud.

2. Login Behavior: If a user typically logs in during the day from home but suddenly logs in late at night from a different country, this could be a sign that something is off with their account.

3. Device Usage: If someone usually uses their smartphone but suddenly tries to log in from a public computer, it might mean their account is at risk.

4. Geographic Location: If a customer always makes transactions from one country but suddenly starts doing so from multiple places, it could suggest fraudulent activity.

5. Session Duration and Frequency: If a customer suddenly spends a lot more time on their account, it could mean they are facing issues, while a big drop in usage might mean they are unhappy or have abandoned their account.

6. Interactions with Customer Support: If a customer contacts support several times about the same problem, it may indicate they are frustrated and need more help.

7. Activity on Financial Products: If someone who usually applies for loans suddenly stops, it might mean they are confused or unhappy with the process, signaling a need for better communication from the bank.

8. Usage of Features in Banking Apps: If a user starts using a budgeting tool more often, it could suggest they are worried about spending, which might help banks offer better resources or advice.

9. Online Behavior on Websites: If someone visits the loan application page but never completes the application, it could mean they need extra help or information to move forward.

Third-Party Data Vs. First-Party Data

Both types of data help inform an organization’s understanding of user behaviors, preferences, and trends.

1. First-Party Data

First-Party Data refers to information that a company collects directly from its customers. This data is gathered through various interactions, such as:

• Website Visits: Tracking how users navigate a company’s website, which is also called website behavior analytics.

• Customer Surveys: Collecting feedback through forms and questionnaires.

• Purchase History: Recording what products customers buy and when.

Since first-party data comes directly from the source, it tends to be more accurate and relevant. Companies can use it to personalize marketing, improve customer experiences, and drive sales.

2. Third-Party Data

Third-Party Data, on the other hand, is collected by organizations that do not have a direct relationship with the user. This data is often gathered from various external sources, such as:

• Data Brokers: Companies that collect information from multiple public records and online sources.

• Market Research Firms: Organizations that conduct surveys and gather demographic information.

• Advertising Networks: Platforms that track user behavior across different websites to create audience profiles.
  

Third-party data can provide a broader view of market trends and consumer behaviors but may lack the specificity and accuracy of first-party data. It can be useful for targeted advertising and reaching new audiences.

How Does Behavioral Analytics Work?

Behavioral analytics works by tracking and analyzing users’ actions to find patterns that reveal these customers’ habits, preferences, and potential risks. Here’s how it generally works:

• Data Collection: First, it gathers data from various sources, like website clicks, login times, transaction histories, and device types. Every action users take generates data that shows how they interact with digital platforms.

• Pattern Analysis: Advanced AI behavior analysis software looks for trends and patterns in this data, so it may notice if a user typically logs in from the same location or usually makes small, frequent transactions.

• Anomaly Detection: Through behavioral data analysis and comparing each user's actions against their usual behavior, behavioral analytics can spot unusual activity, like large, unexpected transfers or logins from unusual locations. These are flagged for further investigation.

• Predictive Insights: The AI behavior analysis also helps predict future behaviors, like which customers might respond well to specific offers or who might be at risk of fraud.

Why is Behavioral Data Important?

Before the “why”, let’s explore the “what”, so what is behavioral data? Behavioral data is information that tracks and records the actions and interactions of individuals on digital platforms, and it comes from many different actions customers take when they use financial services.

This information or behavioral data is crucial for banks and financial institutions because it helps identify unusual behavior that might suggest fraud.

Why Collect Behavioral Data?

Predictive behavioral analytics and behavioral data collection help financial institutions understand the way or the “how” users interact with their products or services. For financial institutions specifically, this data is essential for three main reasons:

• Behavioral analytics enhances security: Behavioral data makes it easier to detect unusual or suspicious activities. If a user suddenly logs in from a new device or location, it can signal possible fraud and this is beneficial for banks for example because it allows them to take quick action.

• Behavioral analytics improves customer experience: By seeing which features customers use most or least, financial institutions can refine their offerings to better fit user needs. In other words, behavioral data can reveal frustrations or preferences, which helps tailor the user experience.

• Behavior analytics helps with personalizing services: With insights into user habits, financial institutions can create personalized products and marketing strategies. This can most probably increase customer engagement and loyalty by offering relevant solutions.

Different Types of Behavioral Analyses in Financial Institutions

Behavioral analyses focus on understanding how people act and why, and there are different main types used in financial services to improve customer experience and strengthen security:

1. Descriptive Behavioral Analysis

• Purpose: To look at past actions and patterns.

• Use: Identifies common customer behaviors, like transaction habits or peak login times. It helps banks understand "what happened" in a clear way.

2. Diagnostic Behavioral Analysis

• Purpose: To understand why certain behaviors happen.

• Use: Digs into root causes of actions. For example, it can help find out why users are abandoning a loan application or why a customer frequently contacts support.

3. Predictive Behavioral Analysis

• Purpose: To anticipate future actions.

• Use: Uses past data to predict what customers might do next, such as likely fraud indicators or possible interest in new services.

4. Prescriptive Behavioral Analysis

• Purpose: To suggest actions to optimize outcomes.

• Use: Recommends specific responses based on customer behavior, like offering personalized financial products or tightening security based on risk profiles.

5. Real-Time Behavioral Analysis

• Purpose: To monitor behaviors as they happen in real time.

• Use: Analyzes current actions to spot unusual or risky behavior immediately. Essential for quick fraud detection and responsive customer support.

6. Comparative Behavioral Analysis

• Purpose: To compare behaviors across different groups or time periods.

• Use: Examines differences, like how high-risk customers behave compared to low-risk ones, or behavior changes after a new service launch.

Tools for User, Predictive, and Network Behavior Analytics

1. User Behavior Analytics Tools

There are many user behavior analytics tools available that help financial institutions gather and analyze behavioral data. Google Analytics is a popular example, which is usually used for websites, this behavioral data platform can also provide insights into user engagement on bank sites, which is valuable for both marketing and behavioral analytics security.

Another example for behavioral analytics tools is FOCAL platform, which helps with:

1. Financial risk assessment

2. Trend analysis & navigating financial complexities

3. Income projection & understanding a customer's future earning potential based on factors like current income, market trends, salary increments, and historical performance.

2. Predictive Behavioral Analytics Tools

Predictive behavioral analytics takes historical data and uses it to guess future behaviors. Financial institutions use this to:

• Spot Fraud Early: Noticing patterns typically leads to fraud, which in turn allows banks to take action before the fraud incident or activity happens.

• Personalize Customer Experiences: Predicting what customers want helps banks offer services that fit their needs better.

For example, if a bank sees that a customer who usually uses mobile banking is likely to respond to certain offers, they can send those offers directly to them.

3. Network Behavior Analysis Tools

These network behavior analysis tools look at how users interact with networks to find any unusual activities. Top products use advanced technology to spot signs of cyber threats.

How Is Behavioral Analytics Used in Financial Institutions?

1. Fraud Detection and Prevention

Behavioral analytics is very helpful for detecting fraud. By studying how users behave, financial institutions can:

• Set Normal Behavior Baselines: Understanding what normal looks like makes it easier to spot anything out of the ordinary.

• Monitor Transactions in Real Time: Behavioral analytics software systems that watch transactions as they happen can identify suspicious activities right away.

For instance, if a customer who usually uses their bank app from their home suddenly tries to make a large transfer from a different country, it can trigger a warning for further investigation.

2. Improving Customer Experience

Financial institutions can use behavioral analytics to enhance the way they interact with customers. By seeing how customers use their services, they can:

• Make Interfaces User-Friendly: Knowing where customers struggle can help improve app and website design.

• Offer Personalized Products: Financial institutions can create custom financial products based on customer behavior, which increases the chance of engagement.

3. Boosting Cybersecurity

With more people doing transactions online, cybersecurity has become very important for banks. Behavioral analytics cyber security strengthens security by:

• Verifying User Identities: By studying behavior patterns, financial institutions can tell if a user is who they claim to be.

• Spotting Unusual Activities: Watching for strange behaviors helps quickly identify potential security threats.

Challenges of Using Behavioral Analytics

While behavioral analytics is beneficial, it does come with challenges:

1. Privacy and Compliance Issues

With laws like GDPR and CCPA, financial institutions must be careful about how they collect and use behavioral data analysis. They need to be clear about what data they gather and how they use it, which often requires solid systems for managing consent.

2. Integrating Data

Putting behavioral analytics into current systems can be tough. Many financial institutions have older systems that may not easily work with new analytics tools. A solid plan for integrating data and updating technology is essential.

The Role of Technology in Behavioral Analytics

As technology keeps evolving, behavioral analytics will continue to grow. Here are some trends to watch for:

1. Behavioral Analytics and Machine Learning

Bringing artificial intelligence and machine learning into behavioral analytics will make it possible to analyze data even better. Technologies, like FOCAL platform, can help improve how we predict fraud and respond to suspicious behavior.

2. Real-Time Analytics

There’s a growing need for real-time data analysis. Financial institutions are increasingly investing in technologies that let them analyze data as it happens, enabling quick responses to potential fraud and better customer service.

3. Personalization at Scale

As data collection techniques become more advanced, financial institutions will be able to provide highly personalized services to customers. This shift toward customized experiences will likely lead to greater customer satisfaction and loyalty.

Conclusion

Behavioral analytics offers a fantastic opportunity for financial institutions to boost efficiency, strengthen fraud prevention efforts, and enhance customer satisfaction. By understanding behavioral data science and using advanced behavioral analytics platforms, financial institutions can protect themselves and meet the changing needs of their customers.