Financial institutions have no shortage of data, and most know that advanced analytics, machine learning, and artificial intelligence (AI) are key technologies that must be utilized in order to stay relevant in the increasingly competitive banking landscape. Analytics is a key component of any digital transformation initiative, with the end goal of providing a superior customer experience. This digital transformation, however, is more than simply digitizing legacy systems and accommodating online/mobile banking. In order to effectively achieve digital transformation, you must be in a position to capitalize one of your greatest competitive assets—your data.

However, getting to successful data analytics and insights comes with its own unique challenges and requirements. An initial challenge concerns building the appropriate technical foundation. Actionable BI and advanced analytics require a modern specialized data infrastructure capable of storing and processing a large magnitude of transactional data in fractions of a second. Furthermore, many financial institutions struggle not only with technical execution, but also lack personnel skillsets required to manage an end-to-end analytics pipeline—from infrastructure to automated insights delivery.

In this article, we examine some of the most impactful applications of advanced analytics, machine learning, and AI for banks and credit unions, and explain how Daybreak for Financial Services solves many of these challenges by providing the ideal foundation for all of your immediate and future analytics initiatives.

Data analysis provides a wide range of applications that can ultimately increase revenue, decrease expenses, increase efficiency, and improve the customer experience. Here are just a few examples of how data can be utilized within the financial services industry:

1. Inform decision-making through business intelligence and self-service analytics:

While banks and credit unions collect a wide variety of data, traditionally, it has not always been easy to access or query this data, which makes uncovering the desired answers and insights difficult and time-consuming. With the proper analytics foundation, employees across the organization can begin to answer questions that directly influence both day-to-day and long-term decision-making.

For example, a data-informed employee could make a determination on where to open a new branch based on where most transactions are taking place currently, or filter customers by home address. They could also determine how to staff a branch appropriately by looking at the times of day that typically have the most customer activity, and trends related to that activity type.

2. Improve collection and recovery rates on loans:

By implementing pattern recognition, risk and collection departments can identify and efficiently target the most at-risk loans. Loan departments could also proactively reach out to holders of at-risk loans to discuss refinancing options that would improve the borrower’s ability to pay and decrease the risk of default.

3. Improve efficiency and effectiveness of marketing campaigns:

Banks and credit unions can create data-driven marketing program to offer personalized services, next-best products and improve customer onboarding, by knowing which customers to reach out to at the right time. Data-driven marketing allows financial institutions to be more efficient with their marketing dollars and track campaign outcomes better.

4. Increased fraud detection abilities

Unfortunately, fraud has become quite common in the financial services industry, and banks and credit unions are investing in new technologies to fight it. Artificial intelligence can be used to detect triggers that indicate fraud in transactional data. This gives institutions the ability to proactively alert customers of suspected fraudulent activities on their accounts to prevent further loss.

These applications of machine learning and AI simply scratch the surface of what outcomes can be achieved by utilizing data, but they are not always easy to implement. Before a financial institution can embark on any advanced analytics project, they must first establish the appropriate foundational analytics infrastructure.

There are many applications for analytics within the financial services industry, but the ability to utilize machine learning, AI, or even basic business intelligence is limited by data availability and infrastructure. One of biggest challenges to the achievement of advanced analytics initiatives is collecting and aggregating data across multiple disparate sources, including core data. In order to make truly proactive decisions based on data, these sources need to be updated regularly, which is a challenge unto itself.

Additionally, this data needs to be aggregated on an infrastructure built for analytics. For example, a banking core system is built to record large amounts of transactions and is designed to be a system of record. But it is not the optimal type of database structure for analytics.

To solve these challenges, Aunalytics has developed Daybreak, an industry-intelligent data mart built specifically for banks and credit unions to access and take action on the right data at the right time. Daybreak includes all the infrastructure components necessary for analytics, providing financial institutions with an up-to-date, aggregated view of their data that is ready for analysis. It offers users easy-to-use, intuitive analysis tools for people of all experience levels—industry-specific pre-built queries, the Data Explorer guided query tool, or the more advanced SQL Builder. Daybreak also provides easy access to up-to-date, accurate data for more advanced analytics through other modeling and data science tools.

Once this infrastructure is in place, providing the latest, analytics-ready data, the organization’s focus can shift to implementing a variety of analytics solutions, such as advanced KPIs, predictive analytics, targeted marketing, and fraud detection.

In addition to providing the foundational infrastructure for analytics, Daybreak also utilizes AI to ensure the data itself is both accurate and ready for analysis. Banks and credit unions collect large amounts of data, both structured and unstructured. Unfortunately, unstructured data is difficult to integrate and analyze. Daybreak uses industry intelligence and AI to convert this unstructured data into a structured tabular format, familiar to analysts. To ensure accuracy, Daybreak utilizes AI to perform quality checks to detect anomalies as data is added or updated.

This industry intelligence also allows Daybreak to create Smart Features from existing data points. Smart Features are completely new data points that are engineered to answer actionable questions relevant to the financial services industry.

Banks and credit unions are fortunate to have a vast amount of data at their disposal, but for many institutions, that data is not always easily accessible for impactful decision-making. That is why it is necessary to build out a strong data foundation in order to take advantage of both basic business intelligence and more advanced machine learning and AI initiatives. Daybreak by Aunalytics provides the ideal, industry intelligent foundation for financial institutions to jump start their journeys toward digital transformation, with the tools they need in order to utilize data to grow their organizations.