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Methods Of DevOps Methodology In The Banking Industry

Financial services, together with companies in the computer industry, may be ranked among the leaders in DevOps practice maturity. When it comes to accelerating innovation and implementing cutting-edge software delivery techniques like agile, continuous delivery (CD), and DevOps, FIs are at the forefront of change. Several requirements for the financial sector are

Increased data volumes
A cross-channel presence that is growing
Secure, quick data transfers
Customer centricity.

These form the design principles for consulting engagements for banking technology domain specialists like Maveric Systems.

What is DevOps

DevOps is a set of methods and technologies for streamlining operational workflows, procedures, and pipelines for delivering software.

What are the top three benefits of DevOps?

Cost Savings. Every industry is trying to reduce costs, and the banking industry is no exception. Here are several ways that DevOps can help save money. Infrastructure as a code allows for implementing template-based solutions instead of wasting valuable development time manually setting up IT infrastructure. Software engineers are more concerned with creating new features than how they are distributed and used. Moreover, reducing infrastructure utilization where it is possible helps bring down costs. Customers pay for what is required at a given time, allowing resource consumption and cost efficiency.
Process Automation. Process Automation through continuous software delivery pipelines is another DevOps component that improves resource productivity, developer quality, and product management and visibility while enabling velocity and scalability. Companies may monitor the status of all DevOps processes and releases and ensure that they can optimize, direct, and control them by controlling all release processes from a single, central platform and automating end-to-end pipelines.
Security. This is the main issue that hindered or worried financial services companies in their efforts to undergo digital transformation. Although DevOps’ rapid delivery method was once thought to be a way to undermine security, several financial firms on the move reported security improvement and quicker recovery thanks to DevOps practices. DevSecOps was introduced as an added benefit to ensure complete security integration throughout the pipeline. As a result, several businesses in the sector began to consider DevOps as a resource to handle security.

Agile Methodologies Promote Digital Transformation

To stay ahead of the competition, most banks now use agile processes. The primary reason for doing this is to meet the regular business demands that result from attempts to enhance the banking experiences of clients. The DevOps teams will primarily be fine-tuning the project planning and execution procedures that support this improvement goal. Applications would be developed using the agile methodology for banking in small, manageable chunks called sprints. Before being deemed executable or finished, each stage is painstakingly tested. The appropriate channels are followed for real-time feedback and course correction before adding more layers of development on top of the supplied component. The ultimate result must be that the customer receives a valuable product in less time than is feasible through any other development procedure.

Reducing errors in service delivery

The team can offer continuous build and integration functionalities using workflow management technologies. On dedicated cloud or on-premise systems, they can provide automated integration builds, minimizing service delivery failures. The final audit trail generates a bill of materials for every build, enabling developers to fix mistakes inadvertently. In broad strokes, it is possible to see what additional tasks, lines of code, and software assets were added to the build. The banking application is continuously delivered across test and production environments thanks to DevOps implementation. Errors are less likely to occur during the product life cycles of each financial service or product. As a result, it takes much less time to get from development to production.

Conclusion

In banking and financial firms, DevOps aids in meeting and exceeding new needs. This method enables one to provide value to the market in a secure, effective, and economical manner. As a result, numerous banking, financial services, and fintech organizations have already recognized the necessity to modernize historical banking infrastructure. The philosophy, equipment, and practices of DevOps have become the means of guiding digital transformation. DevOps techniques address governance, security, risk, and compliance issues and improve the quality of application releases.

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.

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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

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.
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.
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.
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.
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.
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.
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.

Conclusion

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

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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:

Empower front and middle-level business managers with accessible visual insights
Increase organizational knowledge of statistical processes and analyses.
Bring precision to critical decision-making for growth and expansion matters.
Automate and optimize data management efforts across the Enterprise.
Boost the organization’s self-service capabilities.
Free up precious management mind space by practicing management by exception
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.

Conclusion

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

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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.

Customer Service – Optimized service teams must rely on advanced coverage approaches from a deeper understanding of client strategy and needs.
Hyper personalization – Tailored Insights by industry or sector
Digital Operations – Speedy execution and uninhibited ongoing services devoid of silos and bureaucracy.
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.

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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.

People-Related.

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.

Technology-Related

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.

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.
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.
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.
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.

Conclusion

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.

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