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UK PM Sunak to announce more ‘proportionate’ climate response

By Kris Sharma, Financial Services Lead, Canonical – the publisher of Ubuntu

Artificial intelligence (AI) has assumed a growing influence within financial services in recent years, affecting areas such as credit decisions, risk management, fraud detection, and stress testing. And for many fintechs, it has been baked into the process from the outset, to the extent that usage of AI in the fintech market registered $6 billion in 2019 and is expected to reach $22 billion by 2025.

Economic fallout from the pandemic, however, has accelerated the timetable for financial services firms to become mass adopters of AI and harness its predictive powers sooner rather than later. For digitally native fintechs, many of which have already embraced AI and its capabilities, this offers the opportunity to invest further in the technology and capitalise on the tools available to accelerate their journeys.

Fintechs across the world are dealing with the effects of Covid-19 and face an uphill challenge in containing the impact of it on the financial system and broader economy. With rising unemployment and stagnated economies, individuals and companies are struggling with debt, while the world in general is awash in credit risk. This has pushed operational resilience to the top of fintech CXOs’ agendas, requiring them to focus on systemic risks while continuing to deliver innovative digital services to customers.

To make matters worse, criminals are exploiting vulnerabilities imposed by the shift to remote operations post-Covid-19, increasing the risk of fraud and cybercrime. For fintechs, building and maintaining robust defences has, therefore, become a critical priority. Organisations around the globe are forging new models to combat financial crime in collaboration with governments, regulators, and even other fintechs.

The technological advances in data analytics, AI and machine learning (ML) have been driving fintechs’ response to the crisis, accelerating the automation journey many had already embarked on.

A new direction

Until recently, fintechs have used traditional methods of data analysis for various applications, including the detection of fraud and predicting defaults, that require complex and time-consuming investigations.  However, by enabling high frequency analytics across large volumes of data sets and using AI and ML, fintech firms will significantly increase the speed and accuracy of analysis.

Kris Sharma

Kris Sharma

The fintech industry, as a whole, can also capitalise on the huge volumes of data sets already on record, from diverse sources across multiple business units, to train ML algorithms that can automate many of their processes and AI for operational resilience. The technology and tools for getting these capabilities into production keep improving and becoming more accessible to users beyond data scientists and AI experts, enabling fintechs to speed up technological adoption.

Kubeflow, for example, an open source tool created to orchestrate AL and ML workflows running on Kubernetes, is one such solution – simple, portable, and scalable. It is ideally suited to TensorFlow, a comprehensive ecosystem of tools, libraries and community resources that lets developers easily build and deploy ML applications.

Additionally, Apache Kafka, an open-source distributed event streaming platform, can deliver seamless communication at a fast pace, thus enabling rapid analysis of quantitative data to compute the value of risk in real-time. AI and ML in mission-critical, real-time applications, such as detecting financial fraud by correlating payment information with other historical data or known patterns, can leverage Apache Kafka as a scalable and reliable central nervous system for enterprise data.

As with any application running at scale, Apache Kafka requires a significant amount of preparation and customisation on the network, hardware, OS, and at the application level. Maintaining a large deployment can be complex and requires constant monitoring and maintenance. Offloading the complexity of managing Apache Kafka to a third-party technology partner would enable financial institutions to focus on innovation and business priorities rather than spending significant effort and resources on managing infrastructure.

With so much uncertainty in the industry arising from Covid-19 and Brexit, the availability of open source tools and partners allows fintech firms to better deal with the effects, while at the same time enabling them to adapt and transform.

The fintech future

AI and ML solutions have the potential to transform how fintechs deal with regulatory compliance issues, financial fraud, and cybercrime. And by using customer data for greater personalisation, fintechs can continue to offer products and services tailored to individual consumer needs.

As yet, most financial institutions are unsure whether a post-Brexit world will focus on gaining more overseas or UK-based customers. With a data-driven approach, fintechs can see where the opportunities lie and fintechs have only just scratched the surface of data analytics. But as the Covid-19 crisis continues and Brexit uncertainty once again moves up the agenda, moving to a data-first approach will become less of a choice and more of a necessity.

During this time of economic disruption that has deep implications for the financial sector, fintechs need to make AI and ML a core part of their transformation effort to adjust to the new normal, and take advantage of the enabling technologies that can propel adoption more quickly and more efficiently.


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