Predictive analytics is also exceptionally good at detecting risks and alerting you to them before anything goes wrong. Some common use cases include:
- Detect Fraud: Commonwealth Bank uses analytics to predict the likelihood of fraud activity for any given transaction before it’s authorized – within 40 milliseconds of transaction initiation, according to SAS.
- Identify Compliance Issues: Financial services companies are using predictive analytics to enhance the success rate during the underwriting and credit modeling processes. Knowing the probability that a customer will take a specific action in the future enables companies to make decisions before entering into a risky relationship.
At face value, this may seem like the least exciting category of uses for predictive analytics, but looks can be deceiving. For large companies with substantial operating expenses, making even small optimizations can have a significant financial impact. Here are some common ways to use predictive analytics to optimize operations:
- Avoid Unexpected Large Expenses: Companies are using advanced analytics to predict the occurrence of extraordinarily expensive events so they can be adequately planned for and avoided.
- Maximize Resources: It’s not easy to understand where to deploy resources and capital to maximize return on investment. Predictive analytics can be used to identify excess capacity and where to implement it to propel the organization forward.
Top Obstacles to Using Predictive Analytics
Though the benefits of using advanced analytics are clear, that doesn’t mean the road will be easy. Leaders looking to invest in predictive analytics will face significant challenges, including:
- Regulation and data privacy concerns: This is a hot issue right now with the grace period for the GDPR regulations ending. If you service clients in impacted regions, it’s critical to follow the requirements. Predictive analytics requires large datasets, so you may need to be creative in finding ways to developing models that don’t rely on identifiable customer information.
- Legacy systems make it difficult to introduce new technologies: Disparate systems lacking interoperability make it challenging to set up the necessary tooling to harvest data efficiently. If you fall into this category, it’s an excellent opportunity to create a roadmap for updating your legacy systems as part of your long term strategic roadmap for adopting predictive analytics. It's worth thinking through whether a data warehouse solution would be right for your business.
- Data readiness: Finding the data is rarely simple for firms with various account structures and naming conventions. For predictive analytics (or any kind of machine learning) to be effective, it’s important to have accurate, complete, and properly labeled data. Don’t worry if a lot of your data is unstructured (text, images or audio). Thanks to advances in image recognition, natural language processing, and audio recognition, predictive analytics models can use unstructured data to inform decision-making.
Despite these risks, the risk of inaction is even more significant. In the next 5 years, winners and losers will be determined by their ability to use data analytics. If you’re unsure where to start or how to prepare, we can help. Our advanced analytics team can provide strategy, hands-on engineering, and machine learning expertise. Get in touch.
DIRECTOR OF ENGINEERING
As Director of Engineering at Very, Jeff McGehee works with clients to build powerful Internet-connected products.GO TO PROFILE