“What’s going on in Banking in 2025.”, The title of a recent report by Cornerstone Advisors certainly grabs attention. But what does it say? Happy (er) days are here again. According to this report by Cornerstone Advisors, 80% of the bankers surveyed were optimistic about the above, based on the economy, industry, and regulatory environment.
Even though the year seems like a happy stretch, we cannot do away with the fragile and sensitive nature of the finance industry. The report subtly marks the need for financial institutions and their technology providers to pay attention to the challenges ahead.
Talking about challenges, data is one of the most promising assets the banking industry possesses, but how banks are leveraging this data and advanced analytics for an impactful outcome is the answer we need to seek. In this blog, we will explore the same.
Unlocking Value Through Data
Banking is more of a people-centric industry. And when there are humans involved, it indicates vast amounts of data generation. With every interaction, transaction, and behavior, it leaves a digital footprint, creating big data which is categorically defined as large diverse sets of information. Sounds like chaos, right? On Brightside, when this data is analyzed using advanced analytical tools, it becomes one of the strongest and most unparalleled assets for banks. It enables banks to offer personalized experiences, mitigate risks, and make smarter business decisions.
Banks are embracing technology and are keen on using advanced tools to transform their operations. In simple terms, banks want to use their data to make better business decisions.
Why does Data matter to Banks?
Data has evolved dramatically in the banking industry. Traditionally used for producing reports, data now has an altogether newer personality. The focus has now shifted toward extracting valuable insights from data to:
- Enhance customer experience
- Strengthen risk management
- Improve operational efficiency
- Enable data-driven decision-making
In the rapidly growing digital era, banks come across a variety of challenges, such as high volumes of uncatalogued data, endless data points, and poor data quality, making data more of a liability than an asset. Given these challenges, this is where advanced analytics becomes inevitable for banks.
What does Advanced Analytics in Banking mean?
Advance analytics in banking is quite different from traditional tactics. It is designed to leverage a sophisticated set of algorithms, tools and technologies such as Artificial Intelligence, Generative AI, and Machine Learning to analyze data at unprecedented pace and precision. This empowers financial institutions to extract advanced business insights, enabling smarter decision-making, improved risk management, personalized customer experiences, and greater operational efficiency.
Types of Advanced Analytics in Banking
Banks today are leveraging advanced analytics services to make data-driven decisions across all functions. These analytics techniques help understand the past, assess the present, and predict or guide future actions for better business outcomes.
1. Descriptive Analytics
Involves examining past data to uncover patterns and trends.
Use Case:
A bank reviews customer transaction data to identify spending patterns across demographics, helping tailor marketing strategies.
2. Diagnostic Analytics
Digs deeper to uncover the root causes of past outcomes.
Use Case:
When loan defaults rise in a specific region, diagnostic analytics help uncover whether it’s due to economic shifts, borrower profile changes, or internal process gaps.
3. Predictive Analytics
Uses statistical models and machine learning to forecast future outcomes based on current and historical data.
Use Case:
A bank uses predictive analytics to identify customers likely to churn and proactively offers loyalty benefits or personalized services to retain them.
4. Prescriptive Analytics
Recommends the best actionable strategies based on predictive models and real-time data.
Use Case:
A bank uses prescriptive analytics to suggest optimal investment portfolios for clients based on market trends, risk tolerance, and financial goals.
Emerging Trends in Banking Analytics
Banking analytics relies on a suite of advanced tools and techniques to drive data-driven decisions. Key tools include business intelligence platforms like Power BI and Tableau, along with data management tools such as SQL, Hadoop, and Apache Spark. But there is more than advanced analytics dashboards, advanced analytics systems, and tools for the future of advanced analytics. Let’s find out what:
As the landscape evolves, several key trends are expected to shape the future of data analytics in banking.
Increase in adoption of AI & ML
Just like most industries, AI & ML are set to play a crucial role in the banking realm. As they become more sophisticated, these advanced technologies will increasingly automate tasks, improve predictions and level up the decision-making process for impactful outcomes.
Real-time Analytics
As the saying goes, time is money. The growing demand for real-time banking analytics will empower financial institutions to respond swiftly to market shifts, customer behavior, and evolving preferences.
Rise of Open Banking & API
The expansion of open banking and widespread adoption of APIs is transforming the financial ecosystem. By securely sharing data with third-party providers, banks can unlock innovative partnerships and deliver personalized services.
Banking on the Future of Data
At its core, banking is about people, and every customer interaction tells a story. Each click, swipe, and transaction generate valuable insights that, when harnessed wisely, can transform how banks operate. This isn’t just about crunching numbers; it’s about uncovering patterns, predicting needs, and delivering experiences that truly resonate. In a world where data is the new currency, those who can turn information into intelligent action will lead the charge. As we move forward, unlocking the true power of data isn’t just an option for banks. It’s the defining move toward smarter, more human-centric growth.
At Lera, with over two decades of industry expertise, we understand the heartbeat of banking. Our Core Banking Solutions and AI-Powered Data Services are designed to help businesses unlock the full potential of their data, irrespective of the domain, driving efficiency, personalization, and smarter decision-making.
“What’s going on in Banking in 2025.”, The title of a recent report by Cornerstone Advisors certainly grabs attention. But what does it say? Happy (er) days are here again. According to this report by Cornerstone Advisors, 80% of the bankers surveyed were optimistic about the above, based on the economy, industry, and regulatory environment.
Even though the year seems like a happy stretch, we cannot do away with the fragile and sensitive nature of the finance industry. The report subtly marks the need for financial institutions and their technology providers to pay attention to the challenges ahead.
Talking about challenges, data is one of the most promising assets the banking industry possesses, but how banks are leveraging this data and advanced analytics for an impactful outcome is the answer we need to seek. In this blog, we will explore the same.
Unlocking Value Through Data
Banking is more of a people-centric industry. And when there are humans involved, it indicates vast amounts of data generation. With every interaction, transaction, and behavior, it leaves a digital footprint, creating big data which is categorically defined as large diverse sets of information. Sounds like chaos, right? On Brightside, when this data is analyzed using advanced analytical tools, it becomes one of the strongest and most unparalleled assets for banks. It enables banks to offer personalized experiences, mitigate risks, and make smarter business decisions.
Banks are embracing technology and are keen on using advanced tools to transform their operations. In simple terms, banks want to use their data to make better business decisions.
Banking is more of a people-centric industry. And when there are humans involved, it indicates vast amounts of data generation. With every interaction, transaction, and behavior, it leaves a digital footprint, creating big data which is categorically defined as large diverse sets of information. Sounds like chaos, right? On Brightside, when this data is analyzed using advanced analytical tools, it becomes one of the strongest and most unparalleled assets for banks. It enables banks to offer personalized experiences, mitigate risks, and make smarter business decisions.
Banks are embracing technology and are keen on using advanced tools to transform their operations. In simple terms, banks want to use their data to make better business decisions.
Why does Data matter to Banks?
Data has evolved dramatically in the banking industry. Traditionally used for producing reports, data now has an altogether newer personality. The focus has now shifted toward extracting valuable insights from data to:
- Enhance customer experience
- Strengthen risk management
- Improve operational efficiency
- Enable data-driven decision-making
In the rapidly growing digital era, banks come across a variety of challenges, such as high volumes of uncatalogued data, endless data points, and poor data quality, making data more of a liability than an asset. Given these challenges, this is where advanced analytics becomes inevitable for banks.
Data has evolved dramatically in the banking industry. Traditionally used for producing reports, data now has an altogether newer personality. The focus has now shifted toward extracting valuable insights from data to:
- Enhance customer experience
- Strengthen risk management
- Improve operational efficiency
- Enable data-driven decision-making
In the rapidly growing digital era, banks come across a variety of challenges, such as high volumes of uncatalogued data, endless data points, and poor data quality, making data more of a liability than an asset. Given these challenges, this is where advanced analytics becomes inevitable for banks.
What does Advanced Analytics in Banking mean?
Advance analytics in banking is quite different from traditional tactics. It is designed to leverage a sophisticated set of algorithms, tools and technologies such as Artificial Intelligence, Generative AI, and Machine Learning to analyze data at unprecedented pace and precision. This empowers financial institutions to extract advanced business insights, enabling smarter decision-making, improved risk management, personalized customer experiences, and greater operational efficiency.
Advance analytics in banking is quite different from traditional tactics. It is designed to leverage a sophisticated set of algorithms, tools and technologies such as Artificial Intelligence, Generative AI, and Machine Learning to analyze data at unprecedented pace and precision. This empowers financial institutions to extract advanced business insights, enabling smarter decision-making, improved risk management, personalized customer experiences, and greater operational efficiency.
Types of Advanced Analytics in Banking
Banks today are leveraging advanced analytics services to make data-driven decisions across all functions. These analytics techniques help understand the past, assess the present, and predict or guide future actions for better business outcomes.
1. Descriptive Analytics
Involves examining past data to uncover patterns and trends.
Use Case:
A bank reviews customer transaction data to identify spending patterns across demographics, helping tailor marketing strategies.
2. Diagnostic Analytics
Digs deeper to uncover the root causes of past outcomes.
Use Case:
When loan defaults rise in a specific region, diagnostic analytics help uncover whether it’s due to economic shifts, borrower profile changes, or internal process gaps.
3. Predictive Analytics
Uses statistical models and machine learning to forecast future outcomes based on current and historical data.
Use Case:
A bank uses predictive analytics to identify customers likely to churn and proactively offers loyalty benefits or personalized services to retain them.
4. Prescriptive Analytics
Recommends the best actionable strategies based on predictive models and real-time data.
Use Case:
A bank uses prescriptive analytics to suggest optimal investment portfolios for clients based on market trends, risk tolerance, and financial goals.
Banks today are leveraging advanced analytics services to make data-driven decisions across all functions. These analytics techniques help understand the past, assess the present, and predict or guide future actions for better business outcomes.
1. Descriptive Analytics
Involves examining past data to uncover patterns and trends.
Use Case:
A bank reviews customer transaction data to identify spending patterns across demographics, helping tailor marketing strategies.
2. Diagnostic Analytics
Digs deeper to uncover the root causes of past outcomes.
Use Case:
When loan defaults rise in a specific region, diagnostic analytics help uncover whether it’s due to economic shifts, borrower profile changes, or internal process gaps.
3. Predictive Analytics
Uses statistical models and machine learning to forecast future outcomes based on current and historical data.
Use Case:
A bank uses predictive analytics to identify customers likely to churn and proactively offers loyalty benefits or personalized services to retain them.
4. Prescriptive Analytics
Recommends the best actionable strategies based on predictive models and real-time data.
Use Case:
A bank uses prescriptive analytics to suggest optimal investment portfolios for clients based on market trends, risk tolerance, and financial goals.
Emerging Trends in Banking Analytics
Banking analytics relies on a suite of advanced tools and techniques to drive data-driven decisions. Key tools include business intelligence platforms like Power BI and Tableau, along with data management tools such as SQL, Hadoop, and Apache Spark. But there is more than advanced analytics dashboards, advanced analytics systems, and tools for the future of advanced analytics. Let’s find out what:
As the landscape evolves, several key trends are expected to shape the future of data analytics in banking.
Increase in adoption of AI & ML
Just like most industries, AI & ML are set to play a crucial role in the banking realm. As they become more sophisticated, these advanced technologies will increasingly automate tasks, improve predictions and level up the decision-making process for impactful outcomes.
Real-time Analytics
As the saying goes, time is money. The growing demand for real-time banking analytics will empower financial institutions to respond swiftly to market shifts, customer behavior, and evolving preferences.
Rise of Open Banking & API
The expansion of open banking and widespread adoption of APIs is transforming the financial ecosystem. By securely sharing data with third-party providers, banks can unlock innovative partnerships and deliver personalized services.
Banking analytics relies on a suite of advanced tools and techniques to drive data-driven decisions. Key tools include business intelligence platforms like Power BI and Tableau, along with data management tools such as SQL, Hadoop, and Apache Spark. But there is more than advanced analytics dashboards, advanced analytics systems, and tools for the future of advanced analytics. Let’s find out what:
As the landscape evolves, several key trends are expected to shape the future of data analytics in banking.
Increase in adoption of AI & ML
Just like most industries, AI & ML are set to play a crucial role in the banking realm. As they become more sophisticated, these advanced technologies will increasingly automate tasks, improve predictions and level up the decision-making process for impactful outcomes.
Real-time Analytics
As the saying goes, time is money. The growing demand for real-time banking analytics will empower financial institutions to respond swiftly to market shifts, customer behavior, and evolving preferences.
Rise of Open Banking & API
The expansion of open banking and widespread adoption of APIs is transforming the financial ecosystem. By securely sharing data with third-party providers, banks can unlock innovative partnerships and deliver personalized services.
Banking on the Future of Data
At its core, banking is about people, and every customer interaction tells a story. Each click, swipe, and transaction generate valuable insights that, when harnessed wisely, can transform how banks operate. This isn’t just about crunching numbers; it’s about uncovering patterns, predicting needs, and delivering experiences that truly resonate. In a world where data is the new currency, those who can turn information into intelligent action will lead the charge. As we move forward, unlocking the true power of data isn’t just an option for banks. It’s the defining move toward smarter, more human-centric growth.
At Lera, with over two decades of industry expertise, we understand the heartbeat of banking. Our Core Banking Solutions and AI-Powered Data Services are designed to help businesses unlock the full potential of their data, irrespective of the domain, driving efficiency, personalization, and smarter decision-making.
At its core, banking is about people, and every customer interaction tells a story. Each click, swipe, and transaction generate valuable insights that, when harnessed wisely, can transform how banks operate. This isn’t just about crunching numbers; it’s about uncovering patterns, predicting needs, and delivering experiences that truly resonate. In a world where data is the new currency, those who can turn information into intelligent action will lead the charge. As we move forward, unlocking the true power of data isn’t just an option for banks. It’s the defining move toward smarter, more human-centric growth.
At Lera, with over two decades of industry expertise, we understand the heartbeat of banking. Our Core Banking Solutions and AI-Powered Data Services are designed to help businesses unlock the full potential of their data, irrespective of the domain, driving efficiency, personalization, and smarter decision-making.
