A few decades ago, a simple financial transaction meant putting everything on hold and spending your entire day queuing at a bank. Fast forward to today’s world; you can now deposit, withdraw, and send money in only a few seconds while you go about your business. All this is thanks to modern technology. What’s riveting is that it only gets better. Financial institutions are now taking advantage of the 2.5 quintillion bytes of data created every day to improve service delivery. Through predictive analytics, for instance, banks can achieve a seamless customer experience while at the same time shielding themselves from risks and losses. Here’s what you should know about predictive analytics in financial services.
What is Predictive Analytics?
Predictive analytics is an advanced branch of data analytics that uses data, statistical analysis, and machine learning to predict future outcomes. In other words, it’s the practice of using existing data to determine future performance or results. It’s vital to note that predictive analytics doesn’t tell you what exactly “will” happen in the future. Instead, the technique forecasts what “might” happen in the future based on existing data sets. In other words, predictive analytics works based on probabilities. This is, thus, the main difference between this technique and descriptive analytics.
Predictive Analytics in Financial Services
As noted earlier, there are over 2.5 quintillion bytes of data generated every day. As a way to learn what their customers want and better their service delivery, businesses are now taking the time to analyze consumer data. This data enables a business to learn where consumers spend the most time and associate shopping behaviors. For instance, every time a customer carries out a transaction, the bank collects some data and uses predictive analytics to gain more insight on the customer’s banking behavior. This, in turn, enables the bank to create solutions that are in perfect sync with what that customer may need. As a result, the banking experience gets better with each transaction. Here’re a few applications of predictive analytics in financial services
Transactional analysis within a financial institution often includes the application of big data techniques, or data mining, to improve how banks segment, target, acquire, and retain customers. With advanced large-scale transactional analysis, financial institutions can personalize marketing to a particular customer by understanding which transactional behaviors may trend towards a specific life event. Transactional behavior can help identify customers who may be interested in a new auto loan, help with college tuition, retirement investments, or mortgage refinancing. This insight enables banks to focus their sales and marketing activities to the right customer at the right time. In the past, this type of transactional analysis would take ages. Thanks to new artificial intelligence and machine learning technologies that power predictive analytics, financial institutions can analyze this type of financial data within seconds.
Fraud detection is yet another common application of predictive analysis in financial services. As noted earlier, predictive analysis uses data, statistical algorithms, and machine learning to forecast future outcomes. In the case of fraud detection, financial institutions apply machine learning techniques to find inaccurate credit predictions and fraudulent transactions done online and offline. Other applications of predictive analytics in financial services include:
- Personalized marketing
- Customer spending patterns
- Lifetime value prediction
- Transaction channel identification
- Realtime and predictive analytics
How To Implement Predictive Analytics In A Financial Services Business
1 – Start by identifying the aspect you want to improve
To make the most out of predictive analytics, you must first determine what aspect of the business you want to improve. For instance, if you’re looking to increase customer expenditure on a specific product, then the element you want to improve is “Product A sales.”
2 – Determine factors that affect the aspect in question
Now that you know you want to increase sales on Product A, the next step is determining what will convince customers to purchase it. For instance, consider what influenced customers to buy it in the past. Was it a discount or a “buy one get one free” promotion. Whatever it was, the next step is to use it to create an accurate predictive logic.
3 – Analyze patterns and predict outcomes
Integrate the logic you created above into a data model and generate reports so you can analyze past patterns to predict potential outcomes.
4 – Automation
Lastly, automate the system so it can update itself automatically every time a customer purchases due to a discount. Also, give your sales team access to the system so they can see what’s influencing sales.
Incorporating Predictive Analytics Into Financial Services
The increasing convenience brought about predictive analytics and Big Data adoption in finance and other industries has seen businesses benefit greatly. A company like yours may benefit significantly by incorporating predictive analytics into your business. For instance, through a consumer’s data, a company will be able to know more about their customer’s spending habits and their expectations. This puts a business one step ahead of their competition as they can be primed to deliver in a way that meets and surpasses customer expectations. Reach out to our team at Insight Financial Marketing today to learn how your business can get started with predictive analytics.
Rob Reale is an Associate Partner and National Sales Manager responsible for business development and sales at Insight Financial Marketing. Rob began working in the Mortgage Banking industry in 1990 and currently helps the financial service industry leverage unique and innovative solutions.