AI in Fintech: The safety net

AI can identify malware threats and intrusions while strengthening data for social benefit, according to Johnson Poh. Excerpts from an interview:

One of the most important aspects in Fintech is cybersecurity. How do you see AI helping develop robust security systems?
AI-powered cybersecurity systems can improve security standards by preserving the integrity of systems as well as strengthen the safety of information. Machine learning capabilities can support pre-emptive countermeasures such as training systems to identify new malware. In addition, AI-powered systems can be in continuous round-the-clock operation to detect threats and intrusions in a timely manner.
 What are the three trends to watch out for in the Fintech sector when it comes to AI and Data Analytics?
There will be three key trends that will gain momentum in the adoption of AI and Data Analytics, namely:
(i) Operationalising end-to-end big data pipeline: Many businesses have started to operationalise their end-to-end data driven pipeline running on the modern stack. This has brought about the restructuring of people, platforms and processes to harness data and accelerate the applications of machine learning and AI. In particular, there will be a key focus on machine learning engineering roles in the maintenance and enhancement of in-production data science models and applications, while managing big data and computational resources. At the same time, project management will undoubtedly shift towards agile methodologies, given the continuous deployment of software components to enhance the big data platform.
 (ii) Cross-industry partnerships for data-sharing: Cross-industry data sharing, especially in governments-to-industry engagements, will lead to significant improvements in strengthening government-verified personal data services for social benefit. This can support better customer service delivery in the financial sector. For example, customers applying for banking loans and services may no longer need to prepare thick stacks of documents, and can retrieve their data from centrally-managed data repository to support their applications with ease.
 (iii) Open dialogue on AI governance: While AI technology is ready for adoption, regulations will need to keep pace with the ever-changing technology landscape. Governments are beginning to recognise the need for a governance framework that underpins the effective use of AI. Practicing AI with well-defined governance will ensure that banks can operate and stay competitive, while ensuring that AI solutions remain explainable, ethical and fair to consumers. 
We have seen some of the most popular technology entrepreneurs divided on their view when it comes to AI? What are your views on AI being a threat to humanity?
There has been much discussion about what AI really means. Simply put, there are two distinct types of AI - namely Applied AI and General AI.
Both Applied AI and General AI are premised on the ability of the machine to continuously learn based on feedback and new information. However, there is still a difference. Applied AI is focused on training a machine to perform routine and specific tasks leveraging supervised and unsupervised machine learning techniques while General AI encompasses a deep learning aspect, where the machine is able to process data in ways similar to the cognitive abilities of a human being.
Much of the current applications is anchored on Applied AI, where machines are used to augment human intelligence, rather than replace it. It would generally be quite challenging for AI to displace humans, especially in areas that require creativity and ethical judgement. Nonetheless, we should still ensure that there are relevant frameworks in place to ensure that AI continue to be used in a fair and ethnical manner.
What are some applications of Data Analytics and AI in the banking and finance industry?
ENGAGING CUSTOMERS
Banks and financial institutions have shifted from traditional transact and acquire models to adopting more tailored approaches to deepen engagement with their customers. AI has been used to identify individual transaction patterns from large volumes of data to sift out behavioural insights, anticipate their needs in order to provide better service and customer experience.
 ENHANCING BUSINESS PRODUCTIVITY
AI and data analytics solutions have helped to significantly improve operational efficiency and business productivity. In the area of Anti-Money Laundering (AML), AI solutions have played a significant role in reducing false positive rates so that abnormal patterns can be detected in an efficient manner. With greater precision and automation in transaction surveillance and name-screening operations, resources can be allocated more effectively.
AI may also be used in sifting insights for automated report generation. National Language Processing (NLP) and text mining techniques have changed the way we generate reports for market insights. These AI techniques help to sift out trends and key business topics of interests. This has helped to automate market summaries and issue reports on a regular basis for consumers.
What can delegates expect from your session at the Artelligence Forum 2019?
The session will discuss the relationships among concepts relating to AI, Data Science and Analytics, outline how organisations can empower the effective use of AI and Analytics, taking on the lens of people, process and technology as well as explore potential applications of AI and machine learning in the finance industry.

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