Source: fintechnews.sg
Data science, which involves developing methods of recording, storing and analyzing data to extract useful information and gain insights, has created a drastic change in the financial services industry.
Considered as the core of fintech, data science and its allies in the form of artificial intelligence (AI), machine learning (ML) and big data, have brought the sector to the next level, with now applications in areas including risk evaluation, fraud detection, payments and transaction records, as well as asset management.
In risk evaluation, data science allows fintech companies to assemble faster and more accurate credit evaluation processes; in fraud prevention, it helps enhance detection and prevention processes through real-time monitoring and evaluation; and in asset management, data science is enabling fintechs to crunch huge amounts of data and construct asset management models.
But besides the ability to provide customers with more personalized products and services, evaluate credit risk more rapidly and precisely, and fight threat and fraud in real-time, data and analytics also allow financial services firms to take a much more holistic view of how their businesses are performing, providing them with more complete insights to support strategic decision making.
The International Data Corporation (IDC) forecasts worldwide revenues for big data and business analytics solutions to reach US$274.3 billion by 2022, growing at a five-year compound annual growth rate (CAGR) of 13.2%. This growth will be driven by digital transformation and rising demand for better, faster, and more comprehensive access to data and related analytics and insights, said Dan Vesset, group vice president for analytics and information management at IDC.
Banking and Big Data
The industries currently making the largest investments in big data and business analytics solutions are banking, discrete manufacturing, professional services, process manufacturing, and federal/central government, according to IDC.
Given its potential to simplify financial decision making and enable superior, personalized solutions, data science has become a critical component in incumbent financial institutions’ digital transformation strategy, with executives in financial services, as well as in technology, media and telecommunications, citing AI and big data as two of the top five most impactful technologies in financial services, alongside the Internet-of-Things (IoT), 5G, and cloud computing, according to PwC’s 2019 Global Fintech Survey.
The trend is also reflected in hiring trends with major global banks including Citi, UBS and Morgan Stanley, all beginning to ramp up their data and intelligence capabilities in 2018, racing to hire data analysts, data scientists, data platform mangers, and more, according to a research by data intelligence platform Outside Insight.
With data science rapidly becoming a prerequisite for any finance company looking to maintain a competitive advantage, Refinitiv, a global provider of financial market data and infrastructure, has introduced Refinitiv Learn-It-All-Labs, a series of hands-on, interactive sessions aimed at helping equip the financial services data science community with the latest skills and ML use-cases in bite size 30 minute sessions.
For its first session, called NLP for Capital Markets 101, Refinitiv explored the impact of natural language processing (NLP) and deep learning on financial organizations and the overall industry, and the different techniques the company uses to make sense of its unstructured data. The session also included a live demo of SentiMine, a new Refinitiv Labs project which applies NLP to portfolio management.
Refinitiv’s next Learn-It-All-Labs virtual lab session will take place on May 21, at 8am BST, and will focus on design thinking. In this session, the company will share its design thinking skills and explain how it has applied them to a real-life project called the Data Access Tool.
Key takeaways will include a deep dive into what design thinking actually means, the difference between traditional thinking and design thinking, how to apply a design thinking to a project, and more.