Financial institutions have always competed fiercely with one another. However, today’s market has reached unprecedented competitiveness, even at times bordering on cutthroat. The intense level of competition has caused all the most prominent institutions to rethink their strategies. They are scrambling to stay relevant in today’s data-driven market. With this in mind, key trends are currently disrupting financial services. The trends will also impact how banks evolve over the next decade. Below, you will find directions that have emerged in recent years.

Data Standardization Is Becoming Key

There is no standard method of storing and sharing data. This means many problems are occurring. The vast amounts of data that are being generated have become too difficult to handle. What is needed is to store, organize and share this information in a structured way. According to business experts like Kirk Chewning Cane Bay Partners located in St. Croix, it is also crucial to maintain high quality at the same time.

Data Scientists Are Shifting Their Focus

Applying big data to the financial industry requires knowledge in more than one area. Instead, it demands multiple areas of expertise, including business intelligence, machine learning, predictive modeling, and statistics. What is required is a data scientist who understands the business and the tools and technologies that can be used to analyze data. Professionals need to understand or work with people who know what financial management is to succeed.

Data Management Platforms Needs to Scale

Financial institutions are trying to collect vast amounts of data. However, it is becoming too challenging to manage all of it to allow quick access and analysis. A platform or infrastructure to allow for ample data storage is required. The platform also makes the data easily accessible. This means that it must be able to scale on-demand and perform complex operations in real-time. It should also provide accurate insights into the information at hand.

Learning Machines Are Transforming Banking

Financial institutions can use data science to build learning machines. These machines will learn from the data collected. They will apply this knowledge to the decisions that need to be made. This can transform how banking is done and move into a more automated process.

Data Quality Goes From Correlation to Causation

The biggest challenge with data science for financial services is determining causation when there are only correlations between events. For example, most investors believe there is a correlation between bond yields and stock prices. Still, no one can say with certainty if there is any cause-effect relationship between these two events other than what they see on the charts.

Big Data Management

The rise of big data has created a generation of financial institutions with vast amounts of data at their disposal. The challenge now is to make sense of this data. Institutions also seek ways to turn it into useful information that employees can use in strategic and tactical capacities. Firms need to access large amounts of structured and unstructured data quickly and efficiently. As a result, the most relevant individual may utilize it for decision-making. Individuals are becoming more data-savvy. With this, employees will have greater access to data, both in real-time and historical.

The Rise of Robo-Advisors

Automated financial advice is a market that is expected to grow to. Over the past decade, the most significant impact on consumer finance has come from the rise of the robot advisor. Currently, established players in retail banking are still focused on traditional consumer banking. Still, they must not overlook how much their industry will be disrupted by fintech, particularly robot advice and other online advice offerings.

Big data is expected to grow tremendously in the coming years in financial services. Data science and predictive analytics are the main drivers of this growth. With this increase in demand, new trends and opportunities are popping up. Many of these trends shape how financial institutions can leverage big data for their success. The data is also used in helping consumers make better decisions about their plans.

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