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The world is producing a huge amount of data that will increase tremendously over the next years. Data Analytics methodologies and techniques help to discover useful information and supports decision-making. In conjunction with Quality Intelligence, it helps companies to easily interpret their big data, report on quality aspects of their IT landscape, identify areas of improvements and strengthen their businesses in a competitive market.

Artificial Intelligence Transforming Banking and Financial Services

By Srinivasa Sundar Bandepalli, Swaminathan Sambasivan and Anindya Mukherjee

The financial industry is undergoing its deepest level of disruption in decades. Established models of traditional banking are being challenged by a shift in consumer needs, competition from emerging Fintech companies, technological advancements in IoT, Big Data, AI etc. and a generation of millennials who are demanding a change. To withstand digital disruption, banks and FIs are retooling their operations by adopting RPA and AI in order to thrive in a rapidly digitised and data-driven world.

Knowledge-based AI platforms that combine machine learning together with the deep knowledge of an organisation are emerging at a rapid pace. Various forms of AI are transforming all spheres of banking – from channels and branches to back offices – with chat bot-driven customer assistance, humanoid robots and RPA. McKinsey estimates that the economic impact of the automation of knowledge will reach USD 6.7 trillion annually by 2025.

Driven by AI’s ability to learn, build knowledge and derive insights at lightning speed, understand natural language, and run operational processes at much lower costs, new cognitive solutions are invading the financial services sector. This whitepaper intends to discuss:

  • Factors disrupting the traditional banking arena
  • Banks’ efforts in leveraging AI’s potential to remain competitive
  • Business challenges and concerns around the adoption of AI
  • The benefits of AI in multiple banking segments despite all odds

RegTech – The Confluence of Technology and Compliance

By Jaishree Sambandan, Smitha Rao and Sriram K

Aiming to maintain high levels of financial integrity, the regulators of financial institutions across the world keep enhancing their requirements, restrictions and guidelines. Post- (2008) crisis, the financial services industry is witnessing a flurry of regulatory initiatives across all markets. Banks are facing heavy fines for non-compliance, and have started spending more on compliance & risk management programmes. On a no-choice basis, banks face clear challenges in balancing their spending between regulatory compliance and renewed technology. The complete catalogue of new regulations is projected to exceed 300 million pages by 2020. This is far beyond the capacity of humans to keep up!

“RegTech”, touted as being the new FinTech, serves primarily to reduce the costs of compliance by adopting new technologies to facilitate the delivery of regulatory requirements. With this new methodology, monitoring compliance and regulatory obligations will be easier, swifter, complete and more efficient. While FinTechs focus on providing innovative and speedy financial services, RegTechs are primarily a response to the huge costs of compliance.

The unravelling of RegTech would be fascinating and it would be interesting to see where (Artificial) ‘Intelligence’ meets ‘Compliance’ along the RegTech journey. This paper discusses the areas of regulatory requirements in the financial services sector and the industry solutions that are set to aid the RegTech firms.


Smart Data – ‘Intelligent’ Data as a Driver for Insurance Products of the Future

By Helmut Körfer

Data is gaining importance both in society and in economics. Progressive digitalization in all aspects of life and business is leading to a rapid growth of databases. This also includes new data types, which are currently subsumed under the term ‘Big Data’, as well as all previous data, which are already being actively used in companies. For insurance companies, for example, it will be crucial to use this data as part of their product and premium income development, so that they can react to changes and conditions in the market in a flexible manner and as quickly as possible.

Increasing amounts of data with ever-increasing variety should be analysed as individually as possible and used by the EU General Data Protection Regulation in accordance with legal requirements. In the field of quality assurance, the challenge is that requirements must be specified more and more precisely as well as corrected – this requires a high degree of flexibility and the use of analytical methods. SQS not only supports the implementation of the General Data Protection Regulation, but also provides solutions for the quality assurance of Smart Data and Big Data systems.

This white paper presents the potential for the use of smart data solutions for the insurance industry. In addition, solutions are presented that show how it is possible to test smart data solutions, how to comply with the general data protection regulation and how to specify requirements based on analytical methods as precisely as possible.


Agile Big Data: Efficient Quality Management of Functional and Non-functional Characteristics

By Michael Recktenwald and Helmut Körfer

For reasons of data protection, data abuse and potentially erroneous use in the era of predictive analytics, the requirements and expectations regarding the security and quality of Big Data systems have very much come to the fore. At the same time, however, Big Data projects and the associated systems are expected to deliver quick results and sufficient added value, which is why businesses often forego a Big Data test environment and confine themselves to building up a productive Big Data system. However, we show that the Big Data approach makes sense and delivers benefits in terms of meeting quality and security requirements, provided it is linked with continuous verification and continuous validation. We go on to demonstrate that the quality expectations for Big Data systems can be met more effectively using agile methods rather than traditional ones. In this white paper, we also explain what influence these changes have on the existing test disciplines, roles and test strategies.