Data and Analytics – The Key to successful digital transformation

Data & Analytics - The Key to Successful Digital Transformation

Data and Analytics - The key to successful digital transformation 

Digital transformation is conceptually focusing on implementing firms for the digital era. Businesses should undergo digital transformation in order to reap the benefits from their transactions. Without digital transformation, a company will struggle to meet the needs of its consumers. 

Customers now seek to make their transactions easier and more convenient with the introduction of new technology, gadgets, and business apps. Furthermore, they anticipate that most organizations will adopt these modifications. 

By keeping up with changing consumer needs, digital transformation enables a business to thrive in the future. Only a comprehensive and integrated digital strategy that incorporates technology, infrastructure, and modernization can ensure the success of a digital transformation 

It also necessitates a focus on data and analytics, which are the primary drivers of digital transformation. Data-driven insights help firms better understand their consumers, develop company strategy, and make better decisions.  

Data and analytics are the primary drivers of organizational success in digital transformation. To stay ahead of the competition in the expanding digital economy, businesses must give forward-thinking and innovative insights. According to Forrester, insights-driven organizations set the pace for global growth, expanding by more than 30 percent on average each year. 

The use of data and analytics for digital transformation  

Businesses must adapt to changing consumer expectations. Decisions made quickly and intelligently using data will decide a company’s capacity to compete and grow. Data can be utilized to create new features that help businesses compete in the digital market, as well as to improve the product offers they already have. 

While data gives the necessary facts, analytics offers the perceptions that help with well-informed choices. Together, data and analytics ensure a successful digital transformation by assisting you in resolving important contemporary business issues that could obstruct effective data projects. Among these current business issues are: 

  • Data Silos 
  • Data Quality Issues 
  • Complexities with legacy system/application 
  • Meeting customer’s expectation 

Benefits of data and analytics for your business: 

 Better business decision making 

The primary advantage of data analytics is that it greatly enhances company decision-making and can considerably prevent future financial losses. Businesses can use predictive analytics to foresee potential outcomes of changes to their operations, and prescriptive analytics can suggest how best to implement those changes inside the organization. 

 Streamline Operations 

Businesses can improve operational efficiency with the aid of data analytics. Using useful client data, customized items can be made. Numerous data technologies are available to automate repetitive operations, allowing businesses to use that spare time to complete other jobs that call for cognitive abilities.  

Mitigate risk 

There are risks everywhere in business operations and hence they require an effective risk management strategy or solution to address the common issues. Data analytics plays a critical role in managing an effective risk management strategy. 

Enhance security 

All businesses are affected by data security issues. Organizations can analyse  and visualize relevant data, as well as diagnose the causes of previous data breaches. Organizations can use statistical data models to prevent future attacks. 

Wrapping Up 

We at Nallas provide consulting, engineering, and experience design services to help businesses thrive in the digital age, using a data-driven, customer-centric, future-first approach. With the right tools and technologies, our data and analytics services can unlock powerful data analytics insights to assist you in streamlining your business operations. 

Connect with our data analytics experts here to turn your data into actionable insights. 

Data Management Challenges for financial firms and solutions to overcome it

Data Management Strategy

Data Management Challenges for financial firms and solutions to overcome it

Data is a critical resource for firms who are concerned about customer operations and relationships. After all, it provides valuable information about companies’ interactions with their customers. Specifically, financial services companies have a huge amount of customer data that can lead to increased sales as well as provide insight to operational improvements. Despite potential new revenue and cost reduction opportunities, many financial firms still struggle to gain insight from their data.

Solutions to overcome data management challenges for financial firms:

Data Integration

The exponential rise in data volume over recent years has left financial services firms with a challenge to aggregate, manage and derive value from it.  As more data accumulates, it’s quite common for any firm to become unaware of what data it has, where the data is, and how to use it.

Data integration can help financial services companies manage customer interactions and obtain a “single customer view,” leading to cross-selling and upselling marketing opportunities, as well as new product development insight.  It can also ease complexity and increase the accuracy of satisfying regulatory requirements, adding regularity and repeatability to compliance data programs.

Enterprise data solutions, whether cloud-based data lakes, marts, or warehouse, can centralize and auto-aggregate data across enterprise and social applications, external partners, and the IoT, assuring data privacy and security in their design.  Further, 24×7 monitoring and operations, complete with archival and destruction procedure can manage data to the appropriate scale and relevancy.

With integrated data, financial services firms can 

  • Reduce their manual effort to aggregate and resolve data discrepancies and human errors, decrease the time it takes to prepare data for analysis from months to weeks or days.
  • Build a shorter path to valuable revenue insights.
  • Mitigate risk from outside intrusions and breeches.

Further, with the use of modern data integration technologies, financial companies can onboard their customers and streamline digital payments and transactions without expensive manual processes

Data Analytics

With the use of data analytics, the financial industry can effectively manage credit risks and minimize their exposure to such risks.  AI-powered analytics and consumer personas provide deep insights about customers’ behaviors, enabling banks and financial services companies to continuously evaluate customers’ performance and reduce their risk exposure on a continual basis as credit risk profiles change. 

Aside from credit risks, another leading risk for financial services companies is fraud detection. AI, Big Data, Machine Learning enable financial firms to analyze transactions based on behavioral analytics, predictive analytics and machine learning models, helping financial firms detect frauds effectively and proactively. 

Chartering an analytics program demands the early definition of business goals, revealing which insights are needed and in which priority order, at which point the implementation of analytics and visualization platforms and tools, and data science solutions can actualize the vision.

Nallas helps financial firms convert data into strategic assets.  We facilitate significant cost savings for your organization by eliminating expensive manual processes. By enabling informed and accurate data insights, our services will lead you to overall better decision-making (less rework) and “Flight Control” for key strategic initiatives.

At Nallas, we care about what you care about and at the end of the day, what is most important – the business outcomes you are driving towards. This is your metric of success – and so it is ours.  

Check more about our Data Engineering services here

Significance of machine learning for fraud detection in digital banking

Significance of machine learning for fraud detection in digital banking

Banking fraud has been an ever-growing issue with far-reaching consequences for the banking and financial industry in terms of financial losses and credibility.

Cybercriminals are getting even smarter day by day and leveraging technical advancement for their own benefits. Hence there is a clear need for better fraud detection models and robust fraud management models in the banking and financial industry given that most transactions are digital nowadays.

In recent years, there has been an exponential increase of transactions due to the emergence online payment systems. Proportionately, fraudsters have become smarter and savvy; traditional bank fraud detection systems are no longer sufficient enough to combat sophisticated fraudulent behavior. To keep pace with continuously evolving technology and the complexity and volume of cyber intrusions, machine learning and other predictive algorithm analyses are greatly beneficial.

How does machine learning work to detect fraud?

Fraud detection and risk management programs using machine learning start by gathering and grouping previously recorded data delineating between legitimate and fraudulent transactions.

It is good to have as much data as possible, so that theoretical fraud patterns can be continuously verified and re-verified. Once fraud patterns are confirmed, the machine learning algorithm is “trained,” or taught to group, analyze, and report on data according to the established fraud pattern. The program is now capable of routinely and efficiently identifying fraudulent transactions from legitimate transactions in a bank’s broader fraud management framework. The algorithm will need to be updated from time to time, as the profile of threats and intrusion attempts continue to evolve over time.

Benefits of machine learning in fraud detection 

Many modern analytics are still largely dependent on humans to analyze data and detect suspicious transactions and fraudulent activity. This dependency is prone to issues like slow speed and human error. The use of machine learning can solve these issues, avoid cost through the use of technology versus human resources. Specifically, machine learning benefits include: 
Speed – Machine learning algorithms have the ability to continuously collect and analyze new data in real-time, optimizing fraud detection before intrusion, and avoiding costs of remediation.
Cost Efficiency – Machine learning algorithms can detect subtle changes in patterns and perform repetitive tasks across large amounts of data. Algorithms can analyze thousands of payments per second, which is more work than several human analysts can do in the same amount of time. 
Scalability – As previously mentioned, the more data, the better the process. The program improves as more data comes in, enabling it to detect fraud faster with better accuracy. 
Accuracy – Machine learning algorithms can be trained to analyze and detect patterns across seemingly insignificant data. They can identify subtle or non-intuitive patterns which would be difficult, or maybe even impossible, for humans to catch. This increases the accuracy of fraud detection, meaning that there will be fewer false positives and frauds that go undetected. 
Final Thoughts:
Nallas can be a reliable technical partner in the digital transformation of banking businesses. We combine best practices of digital engineering & industry expertise to help clients drive digital transformation at scale. With an elite team of engineering professionals, we provide cutting-edge solutions to overcome security breaches for the banking and financial industry.

Benefits of the Digital Transformation in the Insurance Industry

Benefits of digital transformation in insurance industry

Benefits of the Digital Transformation in the Insurance Industry

Digital transformation is changing the landscape of the insurance industry. Consumers expect insurers to provide fast and easy access to the core functions of insurance operations, as well as to insurance products themselves.  Frictionless interactions with pricing, underwriting, and claims processes demand the use of digital technologies such as AI, ML, Big Data, Predictive Analysis and IoT to provide better value and keep up with consumer needs.  

Following is a list of benefits to be gained through the digital transformation of insurance providers, all of which begin with a foundational enterprise data warehouse and its possibilities for predictive and real-time analytics.  Implementation of these items has shown to reduce overhead in the auto insurance industry by as much as 30% (Source: McKinsey), while dramatically improving customer satisfaction and even reducing payout on fraudulent claims.

Personalized Policies

What if you could more deeply understand the needs of your consumers, to tailor your marketing and outreach specifically to their life circumstances?  The use of data in both public and private databases can help you customize your sales pitch and consumer interactions by offering you insights on their life events and other behavioral characteristics.  Has your prospect recently purchased a new home or taken a new job? What are his or her interests, hobbies, or brand loyalties?  Which devices are they inclined to use?  AI, including chatbots, can help you personalize your insurance policies and the channels through which you market them to the consumer.

Precision Underwriting

Similarly, the automated collection and analysis of important consumer data can yield more precise pricing guidance.  External data sources and the IoT can provide data on the physical behaviors of your consumers, ranging from buying habits and driving behaviors to fitness routines and medical treatment adherence.  What if you could automatically and more accurately assess risk in the pricing of your coverage without burdening your consumers with paper questionnaires and physical examinations?  AI in tandem with your data integrations can inform and enhance your underwriting algorithms.  Even leading insurers have seen loss ratios improve three to five points, new business premiums increase 10 to 15 percent, and retention in profitable segments jump 5 to 10 percent (Source: McKinsey).

Shorter Wait Times, Transparent Status, and Automated Payments

Personalized policies and precision underwriting are only as good as the speed in which they can reach and be purchased by the consumer. It is imperative that insurers incrementally migrate towards a cloud-based environment of workflows, APIs, and third-party data integrations, where the benefits of the digital world can be experienced by the consumer.  A cloud-based environment can reduce the cycle time of application, underwriting, and claims processing from days to minutes, rendering select call center and back-office functions defunct over time.  Cloud-based payment processing for automated, direct deposits versus manual check-cutting and mailing can further reduce overhead and delight the consumer; the efficiencies are endless.

Fraud Detection

An estimated 5 to 10 percent of all claims are fraudulent (Source: McKinsey). According to the FBI, this costs US non-health insurers more than $40 billion per year.  Authenticity and ownership of goods, police and medical reports, as well as identities are mostly verified through manual, reactive, human-based operations. With a foundational enterprise data warehouse and integrations, and shift towards a cloud-based environment, AI, automation, and the possibilities of Blockchain technology can be used to accelerate fraud detection with a higher rate of mitigation and lower incidence of payout.

Ensure a successful digital transformation with Nallas

At Nallas, we can help you predict the magnitude of your digital aspirations, during and after implementation.  We can organize and prioritize your projects based on ROI– investments inclusive of top-notch resources and bite-sized but impactful scope.  We offer a stable low churn, low management overhead, and a flat/self-governing delivery team of seasoned experts with deep data and cloud engineering skills. 

At Nallas, we care about what you care about and at the end of the day, what is most important – the business outcomes you are driving towards. This is your metric of success – and so it is ours.  

Check more about our digital transformation services here.