Utilizing predictive analytics to identify clients who are at risk of becoming delinquent. Lets understand this in simple terms how this concept works, taking example of a Credit Union.
How Predictive Analytics Can Help Credit Unions Collect Money
Lets say a credit union is like a big piggy bank where people keep their money and sometimes borrow money when they need it. When people borrow money, they promise to pay it back, but sometimes they have trouble doing that. Predictive analytics is like a smart helper that helps the credit union figure out who might have trouble paying back their money soon.
What is Predictive Analytics?
Predictive analytics is a way of looking at past behavior to guess what might happen in the future. It’s like using clues to solve a mystery before it happens.
For example:
- If you see dark clouds, you might guess it’s going to rain.
- Google Maps predict travel time based on current traffic conditions, historical traffic patterns, and other factors.
- Wearable devices and health apps use predictive analytics to track your health metrics and identify potential health risks.
- Email spam filters use predictive analysis to identify and block unwanted emails.
- Streaming services like Netflix and Amazon Prime Video use your viewing history and ratings to recommend movies you might enjoy.
How Does It Help?
- Spotting Trouble Early:
- The smart helper (Predictive Analytics) looks at clues like how people have paid their bills before. If someone starts paying late or has less money in their account, the helper guesses they might have trouble paying in the future.
- If someone’s credit score goes down, the helper also knows they might be in trouble.
- Sending Helpful Reminders:
- Once the helper spots someone who might struggle, the credit union can send them friendly reminders to pay their bills on time.
- They might also offer help, like easier ways to pay or advice on managing money better.
- Using Resources Wisely:
- The credit union can focus on helping the people who need it the most, instead of treating everyone the same. This makes their work more efficient.
- They pay special attention to people whose credit scores have dropped because they might need extra help.
- Creating Easy Payment Plans:
- The credit union can make special payment plans that fit each person’s needs, especially if they’re having a hard time. For example, they might let someone pay smaller amounts for a little while.
- If a person’s credit score has gone down, the credit union might offer even more flexible plans to help them out.
- Building Good Relationships:
- By helping people before they get into big trouble, the credit union shows that it cares. This makes people trust and like the credit union more.
- Talking to people about their credit scores and helping them improve can make them feel supported and valued.
Example Story
Think of Jane, who usually pays her bills on time. But lately, Jane has been late with her payments, and her account has less money. Her credit score has also dropped. The smart helper notices this and tells the credit union.
Here’s what the credit union does:
- Friendly Reminder: They send Jane a message reminding her to pay her bill soon.
- Offer Help: They tell Jane about special plans to make paying easier or offer advice on managing her money.
- Keep an Eye: They check back with Jane to make sure she’s doing okay and staying on track.
By doing this, the credit union helps Jane avoid big trouble, and Jane feels happy and supported.
Popular Predictive Analysis Software tools
Here are some tools that can help credit unions leverage predictive analytics to reduce aging receivables:
- SAS Analytics:
- Offers advanced predictive analytics capabilities.
- Provides robust data management, statistical analysis, and visualization tools.
- IBM SPSS:
- Known for its powerful statistical analysis and predictive modeling features.
- User-friendly interface suitable for complex data analysis.
- Microsoft Power BI:
- Offers data visualization and business intelligence capabilities.
- Integrates with various data sources and provides predictive analytics through its AI capabilities.
- Tableau:
- Excellent for data visualization and real-time analytics.
- Can be integrated with predictive analytics tools and platforms.
- RapidMiner:
- Provides a comprehensive platform for data science and machine learning.
- User-friendly and supports end-to-end analytics workflows.
- Alteryx:
- Focuses on data preparation, blending, and advanced analytics.
- Simplifies the process of predictive modeling with a drag-and-drop interface.
- Google Cloud AI and Machine Learning Tools:
- Offers a suite of tools for predictive analytics and machine learning.
- Scalable solutions that integrate with various data sources.
- R and Python:
- Open-source programming languages with extensive libraries for statistical analysis and machine learning.
- Suitable for custom predictive analytics solutions.
- Qlik Sense:
- Data analytics platform that provides self-service visualization and discovery.
- Integrates predictive analytics to enhance data insights.
- H2O.ai:
- Open-source platform for AI and machine learning.
- Provides tools for building predictive models and deploying them at scale.