Displaying search results for ""

Digital Banking Relevance in India

Digital Banking Relevance in India

India’s digitally-powered convenience economy is rapidly becoming the new normal whether it is e-commerce, transport or food. It is against this backdrop that the country’s banking and financial services industry is also leapfrogging into a new digital era.

Today, most traditional banks offer their customers web and mobile sites. When it comes to digital payments, new age fintech startups have defined the space with millions of active users, incredibly user-friendly interfaces and extremely fast service deliveries. While traditional banks initially treated fintech startups as competition, today the mantra is collaboration and co-creation.

Between the two, they’ve ensured banking and financial services are among the earliest adopters of digital strategies. Still, the country’s BFSI digital revolution has only just begun and several aspects are yet to be digitized. The future that the BFSI industry is likely headed toward holds in store the rise of the digital-only bank. It also begs the question – will the bank account remain the cornerstone of all transactions in the future too?

India has over a billion mobile phone connections, nearly as many connections as the population of the country. Of those, the smartphone user base is expected to grow to 520 million users by 2020, predicts a report on digital payments by research firm BCG and Google, according to an Economic Times article. The case for building digital-only solutions is strong, therefore, but it is imperative to examine how mobile banking has worked out so far, what a customer’s future expectations are likely to be, and the challenges of digital banking.

Smartphone owners have their mobile phone either on their person or no further away than 6 feet at most at all times. This, along with the rise of the likes of Google Wallet and Paytm, has pitted traditional banks against the might of technology giants including Google, Apple and Amazon. For now, traditional banks still have the upper hand because all transactions are, at the crux, tied to a bank account.

Whether that will continue to be the case remains to be seen. Banks have invested heavily in mobile banking to both retain existing customers and capture new ones. They initially used mobile banking to let customers check balances, transactions and statements etc, but the kind of services they offer has since evolved. Today, customers can transfer money within and outside the bank, pay bills, recharge their phones, add payees, use mobile wallets and perform several other tasks on their devices. Even so, finance ranks a low 15 out of the average 18 apps that every user downloads on their phone.

If that metric were to improve, the banking and finance industry has to stay abreast of customer expectations in future too and work backward to integrate it into its current digital strategy. That future could involve several additional areas of BFSI being digitized – from personal finance management and card activations to cardless ATM transactions and social media integration.

But there are challenges. For starters, not all mobile banking users download a bank’s app and that skews the data on app versus website usage. The perception of mobile usage also differs globally based on region and age. For Gen X, mobile banking is just an extension of the traditional bank while Gen Y takes a relatively more mobile-first approach. But Gen Z, waiting in the wings, will be the truly mobile-only generation and will take connectivity and online presence for granted. It is for this generation that a digital-only bank probably makes the most sense. Of course, experiments on that front have already begun with Singapore-based DBS’ Digibank, which was launched in 2016. For Gen Z, digital banking cannot merely be an additional feature but a fully-integrated mobile experience in which customers use their smartphones to do everything from opening a new account and making payments to resolving credit card billing disputes.

Traditional banks could lose up to one-third of their market share to digitally-oriented competitors or non-banking competitors by 2020, according to a study by Accenture. Still, jumping headlong into creating digital solutions isn’t the answer. Banks have to consider internal complexities, including the risk of cannibalizing existing business and prepare to transform their culture into one that is more agile, creative and flexible.

In order to build successful digital banking businesses, they have to focus on a few key areas. Well over half of the revenue pool in India comes from current and savings accounts (CASA) and digital strategies should be aligned accordingly. Customer experience must be constantly refined using research and deep real-time understanding of behavior and pain points. New technology and its architecture must be fused with an individual bank’s design, brand, and its business model.

Collaboration and partnerships between banks and startups is the new norm and must continue for the ecosystem to grow and evolve. Banks could consider building a two-speed IT operating model to implement the test-and-learn approach and shorter release cycles – one is the traditional slower but more secure and stable legacy back end, and the second is a rapid, flexible, customer-centric front end. Finally, taking a cue from the pages of the convenience economy startups, banks in the digital era should place due emphasis on marketing their products and services more creatively.


Test Data Management Risks

Test Data Management Risks


Test data management risks and solutions

Providing quality test data is a challenge across the software testing life cycle. A significant amount of resources is spent on creating, maintaining, and archiving test data using manual and semi-automated processes. Using direct copies of production data without de-risking it may result in the exposure of sensitive customer data and financial data, thereby violating regulatory and compliance directives.

Acquiring de-risked high-quality test data faces the following challenges:
1. Distributed Environment – In the current outsourcing model of development, different stages of the software development lifecycle are executed across multiple locations and data, which are to be seen and worked on their entirety. Banks have to deal with their data environment going outside its premises and resolve confidentiality and security problems. Also, if you operate with an Agile approach where time to market is very critical, there is very little additional time available for data generation.
2. Data Complexity – Often, testing teams have to work with different types of data stored in multiple environments in the legacy back-end. For assurance purposes, this data needs to be unified in a central repository. This is a big challenge which is often complicated by the fact that there is little documentation on the relationship between databases, and how to connect them.
3. Differences in Types of Testing – As different types of testing, such as user acceptance testing (UAT)system integration testing (SIT), performance testing, etc., require different types of data, it is imperative that the effort spent by testers to prepare test data is minimized while at the same time the results ensure maximum coverage and volume in the correct format wherever needed.
4. Data Security and Confidentiality – Moving confidential data to the test environment is a risky proposition. With regulators imposing strict compliance norms on banks, the need for effective data masking becomes all the more important.

The solution that addresses these challenges need to ensure the following: unification of data from multiple sources, provide copies of production data, generate data for code coverage, de-risk production data based on regulations and compliance requirements, de-duplicate and reuse data across multiple test environments, effectively categorize and establish relationships between databases. More importantly, all this has to be done without any loss in data quality or integrity. The solution must also allow data to be provisioned to multiple locations across the globe if needed.

Data Profiling & Categorization

This involves identifying relationships between different types of data at the source level. As a best practice, it is not recommended for the production data to be used for the data discovery process. Disaster recovery database or data backup can be used as source data for data profiling and categorization. This process can be done manually as well as through tools such as IBM Discovery, Oracle EM, and CA Test Data Manager. In addition to data profiling, it is ideal to categorize the data based on its business nature and usage. This will help to categorize the data properly as transaction data, financial data, master data, etc., and prepare the data for the next steps in test data management.

Data Masking

It is best to first consider which type of data masking would suit our current goals. There are different types of techniques, such as substitution, shuffling, user-defined function, etc. for effective data masking. For example, name columns can be masked using substitution technique to represent data with another meaningful name, whereas for functional data credit card numbers – where simple substitution or scrambling will fail in functional tests, it is better to use algorithms such as Luhn’s to generate functionally valid credit card numbers.

Data Generation

There are scenarios – such as performance testing, testing an enhancement that is absent in production, testing negative cases, etc. – where data pulled from production databases may not be sufficient for testing. In such cases, data generation tools such as CA Test Data Manager can be used to synthetically create data that satisfies the requirements (in terms of volumes/business rules) and can substitute production data. Also, since it is synthetically generated, the data can be used in different environments without violating regulatory or compliance guidelines.

Copy Data Virtualization

Extremely useful when multiple test environments need the same set of data, data virtualization enables us to provision data using virtual data environments to multiple test environments. With tools like Actifio and Delphix, virtual data can be provisioned and any change made to the data by one user is reflected only in his/her local copy (of changes alone) and doesn’t affect the main copy.

In addition to methods and tools which address the challenges of test data management, there are processes that enable implementing a functional and effective test data management solution. Defining test data request process workflow, documents to be used during requests, SLA and metrics, etc. will facilitate a seamless test data service.

*This article was previously published in Software Testing Magazine on 26 June, 2017