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HOW FINTECH IS LEVERAGING ANALYTICS TO TRANSFORM BANKING EXPERIENCES

How fintech is leveraging analytics to transform banking experiences

The concept of gathering data to improve customer experiences is not novel. Data has long been used to create an accurate picture of their customers, from small-town farmer’s markets to big-city bankers.

Financial services organizations were traditionally product-centric, but as technology evolved, they became customer-centric. The impact of digital transformation in financial services goes beyond data and digitization.

Data Analytics in the Banking Industry has evolved along with the digital revolution, resulting in tremendous growth in the fintech technologies used in banking today. Analytics can be used to increase customer base and reduce costs.

In this post, let’s go with SmartOSC Fintech to explore the importance of fintech analysis in banking and its most obvious applications.

The role of data analytics in Fintech

HOW FINTECH IS LEVERAGING ANALYTICS TO TRANSFORM BANKING EXPERIENCES
  1. Improved Risk Assessments

Big data analytics, in general, can detect patterns and make predictions that can then be used for risk assessment, mitigation, or correction. There are some risks that you want to avoid or reduce. This includes frauds, business failures, and even how customers react to your products and services, which they may not react to in the way you expect.

Whether it is risk assessment, mitigation, or correction, understanding how data and analytics are connected to the overall risk landscape is critical so that the organization is aware of what they are dealing with. Predictive analytics and forecasting can help organizations decide which risks to mitigate and correct using big data professionals.

  1. Customer Orientation

Customer orientation is a pillar of fintech. Financial technology not only assists customers in making better financial decisions, but it also assists banks and other financial institutions in better understanding how their customers perceive them or their competitors.

As a result, financial big data analytics can help with both understanding customers and also includes data collected from devices placed on employees, which can be used to observe behavior patterns, which can then be effectively used to reach out to the right people at the right time through effective and targeted communication channels.

  1. Enhanced Security

Security is also critical. It is critical for data scientists to understand the risk environment in which they work, as well as how big data analytics can be used more effectively to protect their organization’s information assets from cyber attacks. They must learn how it works in order to protect their organization from a variety of threats.

Big data algorithms developed by data scientists, according to data science experts, can assist security teams in identifying suspicious patterns of behavior when used in conjunction with an organization’s security infrastructure, for example, using machine learning, which involves models that use statistical methods and are trained using unsupervised learning algorithms.

Data Analytics Use Cases In Banking

HOW FINTECH IS LEVERAGING ANALYTICS TO TRANSFORM BANKING EXPERIENCES
  1. Credit Modeling

In the banking industry, credit risk modeling is nothing new. Traditional risk analytics models provided insights based on sources of income, loan history, default rates, credit rating, demographics, and other factors. Many other factors must be considered in addition to the standard data.

Consider the case of consumer loans; various dynamics such as social media profiles, utility bills, monthly spending, and savings provide more in-depth insights into default risk. Unstructured data is also important in credit risk modeling. Deeper insights into customers’ financial well-being are provided by AI-based text analysis and consumer personas.

  1. Customer LifeTime Value

The trickiest one, but it appears to be the easiest for anyone in the banking industry to understand. Customer lifetime value provides information about the customer’s future revenue sources, allowing marketers to focus their efforts and reduce churn.

It is difficult to predict how customer behaviors change over time and the major factors influencing their decisions. AI-powered advanced models recognize patterns in data more effectively, providing behavioral insights that humans may be unable to identify.

  1. Risk Analysis and Monitoring

Banks and financial services firms that use dynamic risk models and advanced analytics appear to be more resilient to major external changes. Credit risk, fraud, and liquidity risk are the most important risk models for banks; claims risk and fraud for insurance; and portfolio risk analysis for investment bankers.

The most common risk for most financial services firms is fraud detection, which is constantly changing. Organizations can now analyze many transactions using machine learning, AI, and big data, rather than just historical data. For fraud detection, social media profiles, behavioral analytics, predictive analytics, and advanced machine learning models are combined.

  1. Customer segmentation and personalized marketing
HOW FINTECH IS LEVERAGING ANALYTICS TO TRANSFORM BANKING EXPERIENCES

Personalization requires an understanding of every aspect of the customer. Customers are now inundated with multiple financial products at the same time. How do you know if a customer wants an auto loan? Is the customer planning to buy a house or a car?

The location and timing of your marketing efforts are important in building trust and demonstrating intent to act on marketing messages. You can also reduce awareness marketing efforts by providing knowledge at the appropriate stage of the buyer journey.

  1. AI-powered Virtual Assistants

Consider insurance; a loss or damage may not occur multiple times. It is the single point of contact for showing customers how much you care about them and making the processes easier for them. Customers now prefer self-service options to in-person interactions for processing their requests.

AI-powered virtual assistants add value by answering all information queries about financial services products, services, and eligibility criteria. They are also evolving to validate specific criteria based on updated rules from machine learning models. It would not be surprising if an AI-powered assistant processed insurance claims in minutes.

Conclusion

As fintech companies continue to emerge and disrupt the banking industry, traditional banks are starting to take notice and invest in their own fintech divisions. This is good news for consumers because it means that we can expect even more innovative and transformative banking experiences in the near future. 

One of the most exciting aspects of this trend is the use of analytics by fintech companies. Analytics allow quickly identify areas where they can improve customer experience and provide value-added services that set them apart from the competition. 

Leave your comments below about your opinion on this technology solution. Don’t forget to contact SmartOSC Fintech for more advice on technology solutions.

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