Displaying search results for ""

7 Ways That Data Analytics Can Transform Finance & Banking Sector

7 Ways That Data Analytics Can Transform Finance & Banking Sector

Customer analytics allows financial institutions to conduct more targeted sales and marketing, which in turn increases the likelihood of a transaction being made by your organization. Supporting upselling and cross-selling techniques, banking analytics helps salespeople offer goods and services that complement existing ones and add value to the customer’s banking experience rather than just taking a stab in the dark. Working with Maveric Systems, clients have discovered fresh ways to leverage their internal data analytics capabilities.

Seven Top Use cases for Data Analytics in the Financial Industry

  1. A complete picture of the client from every angle: Applying sophisticated analytics to information about clients, such as the banking products they already use or the family members, a lot can be learned about their motivations and drives. Sentiment analysis can also be used to know how clients feel about your business. By analyzing this data, services that meet the client’s actual requirements rather than assumed can be offered.
  2. First-rate interaction across all channels for your customers. Delivering the correct product or service appropriately – person, place, and time- is a prime example of customization in action. As a result of its ability to make customers feel seen, heard, and understood — all of which contribute to a better overall customer experience — personalization has taken off as a trend in the banking industry, with most customers ranking it as “highly important” in the current financial landscape.
  3. Banking analytics improves the client experience: By making internal operations more efficient, consumers are more willing to engage with banks wherever and whenever on the medium they want. To show respect for their time, this is the ideal approach. Let’s imagine a client is interested in applying for a mortgage. Keeping all necessary documents in one easily accessible location that can be accessed at any time during any interaction saves significant time and effort during the loan origination process.
  4. Bolstering client relationships: Creating better, longer-lasting relationships with clients by offering them the customization they desire, reflecting an appreciation of their time and effort. One of the significant problems facing banks today is customer attrition caused by friction experience and a lack of personalization. When it comes to losing customers, churn analytics is another tool banks may use to their advantage, thanks to advancements in banking analytics. Businesses can pinpoint problem areas by analyzing customer churn, develop hypotheses about customer attrition, and zero in on the most at-risk individuals or groups.
  5. More extraordinary skill in preventing and resolving potential dangers: Data analytics can be used in various ways to help financial institutions reduce their vulnerability to risk. For credit risk management purposes, for instance, customer analytics could categorize clients into subsets according to their propensity to make payments.
  6. Fraud Protection: Predictive analytics can also be used in banking to protect against fraud by identifying unusual consumer behavior based on historical data. Protecting the interests of the consumer and the bank is the goal of using sophisticated analytics for fraud detection and prevention.
  7. Reduce overall operating expenses: In the banking industry, there is an ongoing push to improve efficiency while cutting costs. Financial institutions have historically tried to solve this problem by reducing headcount, but this rarely gets to the root of the problem. To do this, banks need a long-term strategy, not a short fix. This is where banking analytics comes in.



Finally, Data analytics can be used to find new business models, which is especially useful in developing economies. The banking industry’s analytical findings, and customer analytics, in particular, may be helpful to other B2B or B2C enterprises, such as retailers and telecommunications providers. This could be accomplished by developing a new partner ecosystem, disseminating pertinent findings to partners, and identifying opportunities to pool resources to generate fresh commercial possibilities.

About Maveric Systems

Starting in 2000, Maveric Systems is a niche, domain-led Banking Tech specialist partnering with global banks to solve business challenges through emerging technology. 3000+ tech experts use proven frameworks to empower our customers to navigate a rapidly changing environment, enabling sharper definitions of their goals and measures to achieve them.

Across retail, corporate & wealth management, Maveric accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.

With centers of excellence for Data, Digital, Core Banking, and Quality Engineering, Maveric teams work in 15 countries with regional delivery capabilities in Bangalore, Chennai, Dubai, London, Poland, Riyadh, and Singapore.


Data Visualization in Banking is essential for the Finance Sector

Data Visualization in Banking is essential for the Finance Sector

Increased availability of digitally processable data, such as financial data, news, textual communication, and customer data across business domains, means that companies must use every last resource to create meaningful and actionable business insights.

After all, by itself, pure statistical data analysis hardly yields sufficient insight.

Data Challenges and today’s Need for Data Visualization Tools

Today, most data – structures, formats, and types- is highly complicated and multi-variable. Consider customer data with crucial demographic information and financial transactions as an illustration of multi-dimensional data.

Combined with the complexity is the ever-increasing pace and complexity of data that is compelling FIs to modify their standard BI tools and reporting protocols. Visual BI has been increasing in popularity to simplify and synthesize complex data for more straightforward presentation across line management and functional reporting leaders.

To harness the benefits of Data Visualization in its entirety, Banks and FIs partner with deep-domain banking tech experts such as Maveric Data to tailor their data visualization programs

The benefits of Data Visualization  

Given the slew of new technologies and methodologies, enterprises must create a data visualization focussed information strategy before embracing more visual reporting processes. The gains of progressing on a centralized data visualization strategy can be felt organization-wide. They are listed below:

  1. Empower front and middle-level business managers with accessible visual insights
  2. Increase organizational knowledge of statistical processes and analyses.
  3. Bring precision to critical decision-making for growth and expansion matters.
  4. Automate and optimize data management efforts across the Enterprise.
  5. Boost the organization’s self-service capabilities.
  6. Free up precious management mind space by practicing management by exception
  7. Bring rigor to performance management by focussing on role-specific KPIs

Creating a Data Visualization Approach

Evaluate: Begin by examining company processes and interviewing stakeholders to understand roles, objectives, and performance metrics – in short, the enterprise narrative. Once use cases and overall technical requirements are in place, the creation stage begins.

Create: During this phase, quick visualization approaches are created to check if appropriate visual representations convey the current reality.

Design: Next, the user-interface design is planned, and user-experience needs are mapped to it so that effective visualization can be designed.

Execute: Finally, the focus shifts to interpretation, refinement, enhancement, and assignment of user access and consistency of reporting.


As the utilization of data lakes increases, it will impact the speed with which FIs anticipate their client needs and enhance precise targeting to create unprecedented value. Banks today utilize data visualization sciences to unearth profit pockets, identify growth prospects, streamline expenses, investigate business insights, enhance productivity and sales, and make more informed human resource decisions.

The critical question for tomorrow is how to leverage data efficiently and confidently without an unreasonable degree of manual intervention.

About Maveric-Systems

Starting in 2000, Maveric Systems is a niche, domain-led Banking Tech specialist partnering with global banks to solve business challenges through emerging technology. 3000+ tech experts use proven frameworks to empower our customers to navigate a rapidly changing environment, enabling sharper definitions of their goals and measures to achieve them.

Across retail, corporate & wealth management, Maveric accelerates digital transformation through native banking domain expertise, a customer-intimacy-led delivery model, and a vibrant leadership supported by a culture of ownership.

With centers of excellence for Data, Digital, Core Banking, and Quality Engineering, Maveric teams work in 15 countries with regional delivery capabilities in Bangalore, Chennai, Dubai, London, Poland, Riyadh, and Singapore.


Role Of Data Analytics in the Finance and Banking Sector

Role Of Data Analytics in the Finance and Banking Sector

While the Banking and financial sectors continue to change, customer needs are getting more complex. Basis the latest research, the following evolutionary areas surface.

  1. Customer Service – Optimized service teams must rely on advanced coverage approaches from a deeper understanding of client strategy and needs.
  2. Hyper personalization – Tailored Insights by industry or sector
  3. Digital Operations – Speedy execution and uninhibited ongoing services devoid of silos and bureaucracy.
  4. Collaborating to Innovate – Co-creating cross-product solutions, for instance – embedded finance into e-commerce or B2B infrastructure.

To fulfill the above mandates, leading FIs are increasingly dependent on the role of D&A.

The future of customer service

Customer service is changing a lot, moving away from models that focus on the phone and branches to ones that focus on omnichannel interactions, in which customers can move quickly between mobile, phone, chat, and online service channels. The key to providing a high-quality omnichannel experience is a broad customer journey approach that integrates customer interactions across digital and traditional channels.

For Banks to do this successfully, they must leverage Data and Analytics to migrate customers to digital channels by improving their behavioral routing and IVR management.

Hyper personalization

To personalize product offerings, Banks must layer analytics on top of video and audio channels to improve identity verification and optimally personalize. For instance, today’s optimization software enables face-to-face interactions in remote settings without compromising security. Another use is the facial recognition platforms that are frictionless and quick. As the technologies in this field expand, it is likely to change how we use data and analytics in Banking – balance privacy and security with convenience and costs in unprecedented ways.

Digital Operations

One area Data and Analytics are already proving to be game-changing is Digital Operations. As more use cases emerge, AI will transform customer service through automation (virtual agents and contact center agents with real-time interaction tools), advanced knowledge management systems, and Robotic Process Automation. Furthermore, with the advances in Natural language processing and Machine learning, Virtual will get smarter over time. The outcomes will be improved efficiencies, deeper employee engagement, overall cost savings, boosted revenues, and higher customer satisfaction.

Collaborating to Innovate

Addressing clients’ strategic needs is vital for maintaining winning partnerships. What data analytics and the insights thereof do is help client relationship managers anticipate their client’s needs before the clients even know they have them. Furthermore, there is an added benefit – Co-creating products. So instead of building a product and handing it over, leading FIs involve clients and end-customers upfront. This ability is supported by the Data and Analytics capability because that is where the bank’s confidence comes from. For instance, a client may learn of a new product, say, Buy now, Pay later (BNPL), before the competition.

More Use cases

From combating payments fraud, enhancing customer experience, unlocking the SME’s potential, boosting pricing performance via ML platforms, leveraging psychology and analytics to counter bias and reduce risks, and employing AI for advanced problem solving – the use cases for Data and Analytics for Financial Institutions multiplies each day.

The Task Ahead.

As leading Banking organizations explore more analytics-driven impact areas, the logical first step is to map out opportunities and align business cases with necessary front-line adoptions to realize value. This omniscient data layer will only translate into usable information after FIs define, build, and maintain a robust data infrastructure. This is hardly straightforward. After all, the economic and regulatory trade-offs between private, public, and other on-premises solutions are sorted. After that, enforcing an efficient data model supports a business vision, and shared investments are necessary.

Finally, building an effective D&A Financial organization relies on tapping the right talent, skilling and empowering them, and nurturing ecosystem partnerships, including knowledge exchange with the industry and academia.



The Importance of Data Security Policy for Businesses

The Importance of Data Security Policy for Businesses

In a recent study, the average costs of data breaches continue to climb alarmingly. The research by the Ponemon institute reports that it has increased from 2.6% [USD 4.24 million in 2021] to USD 4.35 million in 2022.

On the one hand, it is easy to confuse that data protection is essential only for giant corporations. On the other hand, unsurprisingly, most leading organizations invest considerable efforts in crafting data security policies with well-appointed mandates, a manageable scope, and cross-functional teams.

But even smaller businesses are not immune in today’s climate. And that responsibility falls on the IT departments.

IT’s Role in Data Security Policy

IT departments are seen as custodians when providing the means and methods for creating, storing, sending, and retrieving business-related information. By definition, “protect” includes

  • preventing unauthorized access,
  • the uncontrolled alteration, and
  • unlawful destruction.

While most IT teams understand that data integrity is critical for a business, the challenge comes from the need to strike a ‘wise’ balance between vital data security interests and overhead costs.

Data Privacy and Data Security.

While the two terms are used interchangeably (and share a complementary relationship), they are two separate concepts.

Data security includes the physical protection of Personally Identifiable Information (PII)and how companies protect it from cyberattacks and other data breaches.

On the other hand, data privacy covers policies and procedures safeguarding the collection, storage, and dissemination of PII and a company’s proprietary (often confidential) information.

Elements of Data Security Policy

Data security policy is essential for companies from two perspectives: Human resources and Information Technology.

Both policymakers and reviewers can gain from contextualizing the below elements to their organization’s needs.


In this theme, the Data security policy covers everything from usage norms governing company resources for personnel to adhering to various IT compliance, including establishing appropriate passwords (length and complexity).

Also crucial to the policy is E-Mail administration, and the encryption standards that protect employees, vendors, and customers’ e-mails from phishing and other cyber-attacks are also essential to the procedure.

Another key people element aspect of the Data Security Policy involves Internet-based Social Networking and the extent of using it.

Finally, the policy must call out how employees and other parties are to report breaches and the corresponding investigation protocols.


Most data security rules stipulate the need for the physical and logical protection of IT assets such as servers, routers, firewalls, and more.

After all, rebuilding or replacing a server that has crashed or been compromised is much easier if you can reliably back up, restore, and manage server configurations.

Additionally, access of mobile devices (employees, contractors, visitors, or others) to the parent network is a critical component of an organization’s Data security policy. Next on the list are the encryption mandates – selective or comprehensive.

Lastly comes the management and supervision of access controls for hardware and software, including multi-factor authentication and remote access. This section of the Data Security policy includes keeping track of all software purchases, installations, usage, licensing terms, and expenses.

Data security graphics – information privacy and safe storage technology concept. Word cloud.

Risk implications with an ineffective Data Security Policy.

  1. A breach in security can hurt a company’s reputation, discouraging potential consumers. In today’s age, the severity of data breaches can be experienced in the immediate social media backlashes.
  2. The costly downtime increases the opportunity cost when security breaches strike. A company’s productivity and revenue are negatively impacted. By 2021, the average data breach cost had risen to $4.24 million, a 10% increase from the previous year’s figure. Another survey indicated that when a company uses remote workers (because of a breach), the average cost increases to $4.96 million.
  3. Adding to reputational and financial risk are the legal implications. Be it through penalties, legal action, or even end-customer compensation, suffering companies can sue the ‘data-breached’ company for heavy damages. In some aggravated cases, the losses may extend to patents, blueprints, and other certifications, not to mention customer PII. Adding to the price tag mentioned are also investments in buying extra insurance that covers legal costs.
  4. Along with data loss, the fourth significant risk that a weak data security policy can invite is identity theft and fraud. These losses may include sensitive data such as IP addresses, contact details, and financial details.

Data security graphics – information privacy and safe storage technology concept. Word cloud.


A company’s data is an essential commodity that must be safeguarded at all costs. The complexity and rapid change of data privacy standards that every company must follow further add to the difficulty.

In that regard, apart from firming up official data protection policies, associated safeguards like – staff training, education, data backups, and investments in new-age software security measures are indispensable in the longer term.


How Can Data Visualization Help in the Banking and Finance Sector?

How Can Data Visualization Help in the Banking and Finance Sector?

Real insights come from looking at the world through data and then figuring out how it relates to real insights by talking to customers. Analytics is becoming a competitive necessity for businesses, whether financial services, consumer goods, travel, transportation, or industrial products.

Across all industries, companies that are more analytically driven see three times as much financial growth as their competitors, who are not as analytical. Pharmaceuticals and medical products, insurance, energy, materials, and agriculture are some industries with the most advanced analytics.

But banking, which has been using data for a long time, starts from the best place.

The rising value of insights based on data

In today’s fast-paced business environment, it’s important for finance teams and banking institutions to find data-driven insights and communicate them well. Understanding numbers is still a valuable skill, but it’s also becoming more important to share what the nuances in data mean and why they can be crucial.

From nice-to-have to must-have: Data visualization in Banking Services.  

Today business performance is turned into insights with the help of automated comments available in data visualizations.

Data visualization equips a command center with a customized alert system.

Data Visualization directs executive attention to the most important areas based on the insights gained. It allows to drill down into critical KPIs and corresponding key focus areas thus identified.

Furthermore, inbuilt tagging in data visualization assists teams in workflow assignments.

Present-Day Challenges in Data Visualization. 

Data visualization is compelling when used correctly because it shows a clear turning point and can make a much stronger case than words or simple data charts. But more often than not, analysts spend about 80% of their time loading data and getting it ready and only 20% of their time making analytics.

This means more time is spent cleaning, reforming, and putting together messy, unrelated data than visualizing and analyzing the results. So, the key is automating as much of the data load as possible to make it easier.

How can banks benefit from Data Visualization?

Analytics is a strategic theme for banks, but most have trouble connecting their high-level analytics strategy to a targeted selection and prioritization of use cases and putting them into action in an organized way. Banks use data visualization in commercial, risk, innovation, and technology areas. It helps align the priorities of analytics with the strategic vision.

Integrating analytics into decision-making and enhancing execution. 

One mandate for data visualization is to build advanced analytics assets and teams so businesses can grow. Most banks have been able to start single, stand-alone projects in advanced analytics that work well, but few have turned them into large-scale, efficient operations. Broader use of visualization reveals transformative opportunities and makes it possible to connect with third-party vendors, which allows competence development.

Investing in crucial analytics roles. 

Banks are hiring more data engineers, data scientists, visualization specialists, and machine-learning engineers to meet the growing demand for people with these technical skills. With the growing importance of data visualization, the need for translators is also increasing. Translators are a vital link between business and analytics. They help data scientists understand business problems and priorities and ensure analytics insights are shared with business units.

Allowing the user revolution to happen. 

Banks have a lot of great data sources that can be used in many different ways, but their data practices tend to be narrow and focused on regulations. So, as data visualization practices permeate, high-quality data is more readily usable to build analytics use cases.


As competition in the financial services industry gets tougher, banks must take a data-driven approach to stay in the game. One important thing to remember when using data visualizations in reports and presentations is that too much detail, no matter how it’s presented, makes it hard for people to understand the main points.

It’s important to remember that these reports and presentations are meant to send clear messages. So, instead of putting a bunch of different graphs in a report, finding the one that best conveys the message and then explaining what it means will be much more effective.

Even if more details are needed, less is often more.