Benefits and Challenges of Advanced Analytics in Banking


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In the constantly evolving financial services industry, addressing challenges and adopting a customer-centric approach is essential. Banks can make informed decisions using advanced analytics and offer enhanced services to customers.

Why Do Banks Need Advanced Analytics?

One of the main reasons why banks need advanced analytics is to better understand their customers’ behavior, preferences, and needs. These insights make it easy to tailor their products and services to meet specific needs.

Another critical reason is risk management. Banks deal with many transactions and complex financial products which require a high level of risk management. Advanced analytics can help identify potential risks and take appropriate measures to mitigate them.

In addition, it can help optimize the operations and improve the overall efficiency. It allows banks to streamline operations, reduce costs, and improve their bottom line.

Benefits of Advanced Analytics in Banking

Comprehensive Customer View

Advanced analytics takes into account the customer’s-

  • profile
  • number of operational accounts
  • Financial products they bought
  • Offers they earlier rejected
  • likely future purchases
  • recent and upcoming events in their life
  • relationship with other customers
  • personality traits and choices
  • service inclinations

By analyzing customer data, banks can develop an accurate profile of their customers and identify their preferences, motivations, and needs.

Banks can even track customer sentiments and personalize their sales and marketing strategies using sentiment analysis. This way, they can offer the right product or service to the right customer at the right time.

The data gathered from multiple channels indicates that the customer wants an open investment account. So, instead of pushing for a personal loan, the bank can connect with the customer, offering opening options on investment accounts.

Such levels of insight can help banks refine their sales and direct their efforts in the right direction.

Better Customer Relationships


Customers value personalized experiences that make them feel recognized, heard, and empathized. Lack of personalization can drive customers away. To address this, banks must use advanced analytics to provide tailored recommendations, offers, and services that resonate with each customer.

This results in higher customer satisfaction and loyalty. Simultaneously, churn analytics can help identify customers who are at risk of churning and develop strategies to retain them.

Accurate insights into customer behavior and preferences allow banks to build more profitable relationships. They can also enable them to continually look for ways to simplify or improve the CX.

Risk Management and Mitigation

Banks can use advanced analytics in multiple ways to mitigate risks. By analyzing historical data, banks can predict and prevent fraudulent activities. For instance, banks use anomaly detection algorithms to identify unusual transaction patterns. 

For instance, customer analytics can categorize customers into different groups based on their creditworthiness.

While this helps manage credit risk, it also narrows down the target audience for credit products.

Sopra Steria’s report also states that 64% of banks plan to use financial vulnerability analytics to better protect customers.

Furthermore, predictive analytics can also prevent potential fraud by analyzing customers’ behavioral patterns and detecting anomalous activity.

Also Read: Role of Predictive Analytics in Finance

Minimal Operational Costs 

Banks are under constant pressure to reduce operational costs while simultaneously increasing efficiency. To achieve this, banks need a long-term strategy. Banks can use a combination of predictive and prescriptive analytics to generate strategic recommendations on optimizing existing processes to drive efficiency and lower costs.

For example, banks can make informed decisions about staffing and branch locations by analyzing branch foot traffic and transaction volume data. This will lead to higher cost savings.

Regulatory Compliance

Banks must comply with numerous regulations and compliance standards. Advanced analytics can simplify regulatory compliance by automating data reporting and monitoring processes. For example, by implementing data analytics solutions, banks can effectively track and report compliance with Anti-Money Laundering (AML) and Know Your Customer (KYC) requirements.

Challenges of Advanced Analytics in Banking

Poor Financial Data 

Complex data landscapes with multiple systems and siloed applications make it hard to merge, aggregate, and standardize data promptly.

Advanced analytics that use AI and ML to gather massive data sets need current, high-quality data to produce accurate results.

The issues related to poor-quality data are one of the top reasons for the non-effectiveness of advanced analytics in banking.


Here are a few ways banks can improve data quality using advanced analytics.

  • Address the Severe Data Quality Issues First

Data issues can be overwhelming to resolve all at once. So, it is essential to address the quality issues in targeted increments. First, focus on the common data supporting the core strategic KPIs.

Standardizing this data makes it easy to bring missing, incomplete, and duplicate data to the surface. This will give banks a manageable first target for the data quality improvement project.

  • Identify a Single Source of Truth

Achieving a single source of truth, centralized data sharing offers many benefits. An interim solution is to pursue a “sufficient versions of the truth” strategy. This means making trade-offs between the cost of bad data and the effort needed for added governance. To start this process, it is essential to set data quality requirements.

Determine which level of data quality is critical and which data definitions are most important for financial reporting. These could be functions such as ROI and profitability. Additionally, it’s worth identifying which data can be set aside for use in a later project.

  • Allocate Data Governance 

Banks need to have a framework for data governance to ensure data quality and consistency, even without a single source of truth.

However, implementing and running a data governance program can be time-consuming.

To address this, banks can adopt a distributed data governance strategy. This will have a two-tier function- one that guides which data to govern and how to govern it, and another that will do the actual governing. This framework helps to identify critical data quality issues and where to focus improvement efforts.

  • Deploy a Cloud-based Finance Analytics Software

Cloud-based advanced analytics software helps integrate with more data types and sources compared to on-premise solutions. These software solutions simplify how data is stored, cataloged, aggregated, and accessed. Banks can then assign more time to extract insights and put them to use rather than managing data.

Shortage of Digital Finance Competencies

Some banks lack the technological ability to implement and use advanced analytics in finance. They may also not acquire these tools for fear of resource backlash. This skills gap is often driven by a fear that AI and other advanced automation technologies will render existing jobs obsolete.

As per Sopra Steria’s report, 30% of banks are concerned AI could have a negative impact on jobs in the banking sector.


To overcome these challenges, a multi-pronged approach is required. Banks must focus on upskilling existing, even if new financial talent with the right skill set is scarce. It will help bridge the skills gap and improve their professional development, confidence, and job satisfaction.

In addition to training, it is important to encourage team members to attend industry technology events and actively seek new learning opportunities. All of this will help teams raise their digital skills.

Absence of Executive and Cultural Buy-in 

Adopting advanced analytics in finance is often challenging due to a lack of perception or approach. To successfully push for change, someone needs to lead the initiative, secure funding, and encourage team members to embrace new ways of working.


It’s important to demonstrate the value of advanced finance analytics projects to receive budget and support. This can be achieved by quantifying the benefits, such as ROI and time savings, through autonomous or semi-autonomous analytics.

By doing so, banks can gain deeper insights and generate more accurate predictions. This will help demonstrate their strategic business advice and value.

Another approach is using an available data set to identify advanced analytics pilot projects that can each solve a specific business problem. Successful pilots will provide the evidence and confidence to justify the next or more extensive project.

Convincing teams to move beyond familiar tools and traditional work processes can be difficult. However, this issue can be tackled by letting the teams know how the newer tools save time for them. They should be informed that these tools can automate complex calculations and processes for budgeting, forecasting, and scenario planning.


Allied Market Research Data Analytics in Banking Market report estimates that ‘ the global data analytics in the banking market will increase from USD 4.93 billion in 2021 to USD 28.11 billion by 2031.

Advanced analytics is evolving rapidly and will continue to transform the banking industry. With rising data and the need for personalized services, banks are increasingly using advanced analytics to gain market leadership. They use it to get insights into customer behavior, enhance risk management, and improve operational efficiency.

While advanced analytics will help banks manage risk better, it also helps optimize internal processes, reduce costs, and improve operational efficiency.

As banks continue to invest in advanced analytics capabilities, they can deliver better CX, reduce fraud, and improve profitability.

Check Out The New TalkFintech Podcast. For more such updates follow us on Google News TalkFintech News.

Apoorva Kasam
Apoorva Kasam
Apoorva Kasam is a Global News Correspondent with TalkCMO. She has done her master's in Bioinformatics and has 18 months of experience in clinical and preclinical data management. She is a content-writing enthusiast, and this is her first stint writing articles on business technology. She specializes in marketing technology, data-driven marketing. Her ideal and digestible writing style displays the current trends, efficiencies, challenges, and relevant mitigation strategies businesses can look forward to. She is looking forward to exploring more technology insights in-depth.


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