Data as AI fuel

THE financial technology (fintech) industry in South-east Asia is at the forefront of innovation, redefining the ways in which we transact and manage our finances. As the industry continues to evolve, it faces several significant challenges such as data quality and integration and harnessing the power of artificial intelligence (AI) to structure and unlock the potential of data foundations.

The region has seen a significant shift towards digitalisation – a trend that will certainly accelerate as AI becomes more widely adopted. A study revealed that adopting AI in South-east Asia could add an estimated US$1 trillion to the region’s gross domestic product by 2030. Singapore has the highest rate of AI and machine learning (ML) penetration in fintech, at 5.36 per cent.

It is essential for fintechs to fully embrace AI’s capabilities – and doing so is largely dependent on having a well-organised and accessible data foundation. It enables accurate algorithm training, improves predictive analytics, and enhances decision-making. This organised data infrastructure ensures that AI applications can extract valuable insights, optimise operations, and ultimately address the unique challenges faced by fintech companies.

Furthermore, the integration of AI and ML into data management systems, coupled with a focus on data interoperability, is key. This integration facilitates the development of advanced smart data fabric architectures, enabling fintechs to analyse and visualise data from various sources effortlessly. Access to such diverse data streams allows actionable insights, greatly enhancing the ability to innovate and develop cutting-edge services and applications.

Navigating data challenges

Fintech companies are increasingly recognising the significance of utilising enterprise data and big data analytics.

A survey reveals that 73 per cent of financial service industry respondents prioritise data and plan to invest in big data analytics. In the EY Tech Horizon survey, data and analytics was the second-highest area of technology investment within the Asia-Pacific financial services sector (behind blockchain), with investment growing since 2020.

However, it’s not all smooth sailing. These companies often bump into hurdles such as issues with data quality and trust.

Moreover, shadow data, when not integrated into a data management system, proves useless. Often siloed within one department and conflicting with data in other systems, it exemplifies the urgency for effective data-driven solutions.

Fintech data management and AI

Fintechs deal with vast amounts of financial data on a daily basis, requiring advanced tools to harness its potential. AI is increasingly playing a crucial role by automating and streamlining data management processes, enhancing efficiency, accuracy, and speed in handling financial information.

Singapore is dedicated to cultivating a thriving AI innovation ecosystem. The Monetary Authority of Singapore’s Financial Sector Technology and Innovation scheme features the Artificial Intelligence and Data Analytics (AIDA) grant. This grant supports Singapore-based financial institutions and fintechs by co-funding eligible expenses, with a cap at S$500,000.

The mainstream finance sector is already employing AI and Big Data solutions, where the best-known deployments cover algorithmic trading, market monitoring, and fraud detection. One example is DBS in Singapore using AI to reduce the number of false positives and prioritise alerts, so analysts can dedicate more time to higher-risk activities such as investigating transactions or behaviours that are more likely to be associated with fraudulent or illicit activities.

Despite the advances in AI and ML, the banking and finance sector is still held back from the adoption of more complex models by interoperability problems, legacy systems and a lack of qualified talent.

For the fintech industry, these difficulties faced by banks and financial organisations present multiple opportunities. By leveraging AI, fintechs are able to offer exceptionally high levels of consumer personalisation and provide innovative solutions to these legacy issues, enabling faster and more cost-effective financial services than previously possible.

Overcoming data challenges with smart data fabric

Despite their innovation, one of the most substantial challenges for fintechs remains data quality. Without fast and easy access to the right kind of data, the deployment of AI and analytics will fail to deliver the transformation which these technologies are capable of.

Addressing data challenges in fintech demands a holistic strategy. Opting for a smart data fabric proves most efficacious, seamlessly integrating diverse information from numerous sources, transforming and aligning it for highly data-intensive applications. This approach empowers organisations to thrive with dynamic, batch-oriented data, incorporating cloud and legacy data as needed, facilitated by APIs or web services.

For fintechs, this approach is particularly beneficial.

By weaving together different data sets and providing easy access, data fabrics help generate insights to better understand customer behaviours and offer customised experiences in real-time, thus enhancing customer experience and business results.

Additionally, it supports better regulatory compliance by providing a clearer view of data lineage and usage, which is particularly important in the highly regulated financial sector.

In the face of funding challenges and the complexities of managing increasing volumes of data, fintechs continue to stand at the forefront of financial innovation. Data management solutions are key to their growth and resilience. It’s time for fintechs to turn to data-driven solutions to navigate these challenges for smarter decision-making and to keep up with a fast-changing market.

The writer is country leader of InterSystems