Rapidly develop and deploy sophisticated data applications on modern data architectures with Low-Code/No-Code platform
The Banking and Financial Services (BFS) industry generates vast amounts of customer transactional data and is also subject to the highest level of government regulations. Due to the sensitive nature of the data and regulatory requirements, the industry adopted the highest data security standards and the best storage technologies. However, when it comes to utilizing the data to develop customer insights and new product development, the industry has traditionally been slow to change. But the need for digital transformation, competition from Fintech startups, and new customer demands are changing that. As new financial models such as personalized finance, robo-investing, cryptocurrencies, blockchain, and usage-based insurance take root, the large financial institutions are waking up from their slumber to harness the power of their vast troves of data.
Knowing the customer, building customer loyalty, and developing products that the customer needs are now the most important success factors for any business, but more urgent for the BFS industry. These ideals can only be executed well with the technology and data infrastructure that supports a deep understanding of the customer combined with responsive communications, personalized experiences, seamless multi-channel interactions, and effective operational processes.
Data technology has always been expensive to create and maintain. While the rapid evolution in new technologies has created much-needed capabilities, maintaining legacy infrastructure, the cost of upgrading and the fear of breakage has kept financial companies from leveraging the power of modern data architectures.
Now, modern AI/ML and Big Data technologies are making it easier to organize data, discover patterns, and derive intelligence that can revolutionize operations and sales. But harnessing these technologies can be complicated as rapid evolution and skills shortage can hamstring your organization’s ability to develop and maintain analytical and operational systems.
Enter Datamorph - Kwartile’s self-serve data transformation platform. Datamorph is a suite of tools that optimizes development of modern data architectures. It shields developers from the complexities of the implementation, reduces development time, and allows engineers to focus on uncovering intelligence within the data. The platform abstracts out the complexities of two critical areas – distributed computing and deployment environments – that represent the most complex and time-consuming tasks in the development pipeline.
Datamorph makes it vastly more convenient to develop and maintain powerful data and analytical applications such as Customer Identity and Profile Management systems, Omnichannel Behavior Analytics, Helpdesk Analytics, and Returns Analytics. Additionally, the platform is customizable and extensible, allowing engineers to implement any kind of complex business logic required for building custom business applications.
With Datamorph, a retail business can create streamlined applications to obtain deeper insights into customer behavior, create highly relevant product lines, optimize customer service, vastly improve customer experience, and achieve high-ROI on marketing campaigns– all with significantly reduced spending on IT skills and infrastructure.
Unlike all other industries, the BFS industry did not see the Covid-19 related pandemic upheaval. And thus, the industry will not see a paradigm shift that is likely everywhere else. What will happen though is that the trends of the past decade – digitization, personalization and the shift from RoE focus to product innovation – will accelerate.
The BFS industry does have a lot of catching up to do, as it traditionally had been a laggard in the areas of multi-channel Customer Experience and Personalization.
To be clear, the industry is facing challenges on multiple fronts: Consider the following statistics and trends:
Data over the last 10 years clearly shows that BFS organizations that invested in digitizing their businesses over the last decade demonstrated higher agility and resilience in adapting toCOVID-19-led changes than others. This agility will be needed more and more in the near future, especially as Covid-19 uncovered shortcomings in many Banks’ technology capabilities.
However, legacy infrastructure and data fragmentation across the enterprise continues to weigh down banks’ digital transformation initiatives.
So, what should a financial services business do in order to outperform in the near future as?
Apart from making core product capabilities resilient, there are six areas in which BFS companies need to enhance their capabilities. These are:
From a systems standpoint, all of the above capabilities require mature data integration and management practices supported by the latest data processing software and infrastructure. These include:
More specifically, retailers need to embrace the paradigm shifts happening in data processing technologies, as illustrated below:
The above defined technology stack has great data manipulation and processing capabilities, but requires a highly skilled data team to organize the data, create processing workflows, and build operational data systems.
Add to this, advances in AI/ML systems which require complex training systems and a multitude of data stores for scenarios, and IT development operations can become unwieldy.
It is well known that about 85% of a data scientist’s time is spent on just identifying data sources and pre-processing steps. Similarly, application developers spend much of their time in dealing with the complexities of distributed computing environments (i.e., the multi-cloud),and the complexities of deployment environments.
Kwartile’s DataMorph platform is a Low/No-Code Data transformation platform that reduces the hurdles to developing and maintaining modern data applications. The platform offers a powerful set of tools to create and maintain a modern data processing framework (see Figure 2):
While the Modern Data Architecture solves many problems of data organization, data processing, data accessibility, and data governance, it creates a new set of challenges for developing data applications, including the following:
Due to all these challenges, the data applications programmer is faced with a steep learning curve. The underlying technologies are also advancing rapidly, making it even more challenging for developers to keep up.
What is needed is a way of reducing the quantity of coding required for developing and maintaining data applications, and managing DevOps, cloud deployments, and security frameworks.
Datamorph is a low/no-code platform that is optimized for developing data applications. By using the platform, developers can improve their productivity by 50-90% while reducing operational costs between 20-60%. In the long term, Datamorph provides many other benefits for software maintenance including automated version control, dependency management, and application security management. This leads to high code maintainability and improves the long-term economics of application development even further for companies.
At a high-level, the specific features and benefits of the Datamorph platform include the following:
Also, the platform provides many productivity enhancing tools including the following:
While the Datamorph platform is appropriate for almost any possible data application development scenario, some more representative Financial Services use cases include the following:
Below are three representative case studies that the Datamorph platform has helped implement at BFS customer sites.
It is no understatement that consumer credit information is the lynchpin that powers a vast array of consumer-focused financial products. From home loans to retail purchases, the ubiquitous credit score powers the risk assessment models that make modern finance possible. The Customer Id and long-term profile are critical information repositories required for making credit information products possible. However, the lack of a single consistent view of the customer, as well as alternate views of different segments, limits the market for consumer financial products. As an example, traditional credit scores are not applicable to the underbanked segment such as immigrants, even though their credit-worthiness is quite high. This can be ascertained by evaluating their online interactions and behavior.
Our customer - one of the world’s largest credit reporting and marketing agencies -faced significant challenges in unifying the various sources of data and create usable versions of the truth. Their IT team spends an enormous amount of time and effort in data wrangling and stitching together multiple internal and external systems in an effort to obtain a single, “best version of the truth”, and even then, only with poor results.
The emerging category of Customer Profile Management applications use AI/ML techniques to determine the identity of the customer without having to build traditional and difficult-to-maintain data models. Kwartile’s Datamorph platform enabled our customer to utilize AI/ML libraries and build new data applications significantly faster, with higher quality and reduced coding.
Electronic fraud includes several types of transactional fraud that affects BFS companies. These include credit card fraud, billing fraud, and stock transaction fraud. Managing fraud to low levels not only creates financial benefits, but also improves customer loyalty. Creating an effective fraud monitoring and mitigation process requires processing large amounts of static and dynamic data to uncover patterns. Traditionally, these patterns are first surmised by human experts and then incorporated into the detection algorithms. But the effectiveness of such methods is lower as fraudsters keep changing tactics. Now, with the availability of sophisticated machine learning algorithms and AI, much of the process can be automated.
The Datamorph platform enables BFS organizations to create sophisticated fraud detection and prevention applications with built-in AI/ML and predictive models. The platform also comes with built-in tools to develop analytics applications and operational systems for managing, analyzing, and improving the returns process.
The core marketing functions for a retail business are improving customer conversion rates, reducing churn, and personalizing campaigns to increase revenue. A good customer behavior analytics system is a required to measure and improve the effectiveness of the core marketing programs.
The Datamorph platform enables integration of multiple data sources, near real-time processing of customer interaction data, and development of predictive analytics and recommendation engines, in a fraction of the time required with conventional software development methodologies. Built-in templates and algorithm libraries enable significantly faster implementations.
Risk analysis is core to every financial services business from insurance to consumer credit and retail purchases. Analyzing the risk of a transaction requires a large number of input parameters. Such input parameters include native transactional information such as the size of the order, and the number of currently active trades, and non-transactional data such as social media sentiment or monetary policy news. But as the number of input parameters increase, the complexity of the underlying algorithms increases exponentially, and in turn making the computational hardware expensive or impossible to solve the problem. Data scientists therefore keep the number of parameters to a low number while trying to get the best solution under the constraints.
With the great advances in AI/ML algorithms and hardware chips it is now possible to analyze transactions with even millions of parameters. Cloud-based data processing hardware also reduces the need to build expensive infrastructure. The Datamorph platform enables the development of sophisticated Predictive Modeling applications with built-in or custom AI/ML algorithms and the deployment of cloud-based hardware while abstracting the complexities of both the software development as well as the deployment parameters. This greatly reduces time-to-market and frees up developers to focus on developing intelligent solutions.
The rollout of the projects provided multiple benefits and capabilities to product marketing and sales teams, as well as to the operational organizations including IT and Customer Service.
Kwartile offers a robust set of solution and support services for the Datamorph platform. Our Product Support services offer multiple plans for Enterprises, Software Development professionals, and Partners. The support services include technical help during the warranty period, bug fixes, and regular version upgrades.
In addition, our professional services team offers data strategy, design, and application development services. Kwartile Consultants and Data Engineers possess a combination of deep and practical technology expertise, industry expertise, and strategic product management experience
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