Covering Disruptive Technology Powering Business in The Digital Age

Home > Archives > News > AI Leading Women in the Finance Industry
AI Leading Women in the Finance Industry
August 17, 2021 News


Written by Nur Atikah Yusri, Journalist at AOPG, a data science and machine learning company focused on democratising AI recently hosted a Women in AI panel consisting of three women leaders within the financial industry. Panellists included Wan Ting Poh, the Director of Data Science at Mastercard Singapore, Seema Gaur, CDO and Head of AI at IFFCO-Tokio and Parul Pandey, an Data Scientist. The session focused on the increasing uses and functionalities of AI within the financial industry and the issues of diversity within the field of Data Science.

Companies have been increasingly using AI to enhance and positively impact their businesses. From the insurance industry, Seema illustrated the ways that IFFCO-Tokio, a general insurance company has found ways to innovate and deliver better services to its customers despite the pandemic. The use of AI has benefitted the organisation tremendously in terms of enhancing customer experience, customer retention and the operating ratio of the company. Hugely impacting their bottom line.

Employing the use of AI in the process of customer onboarding in terms of pitching, insurance companies can calculate the propensity of buying products, allowing them to employ targeted selling methods. The use of bots, through which Natural Language Processing (NLP) allows for text-to-speech and IVR functionalities. In terms of claim settlement, the company uses AI to evaluate vehicle damage in accidents using image analytics involving deep learning that estimates the losses and allows for a quick transfer of funds to the customers.

With transactions being done online due to the Covid-19 pandemic, insurance companies have received many forged bills and evidence with regards to claim settlement cases. Using fraud analytics, organisations can detect and prevent these occurrences. AI and machine learning are also being used in the backend processing of invoices. It allows for customers to snap a picture of their invoices for easy input into the system using image analysis and NLP. IFFCO-Tokio has also been able to do pre-inspections virtually for customers looking to renew their policies. As well as, generating and keeping track of customer credit scores allows for a customer-centric response.

On the side of Mastercard, Wan Ting shares that with regards to the use of AI, Mastercard aims to “make use of data and technology to provide customers with different types of solutions that can address the pinpoint in their businesses”. Mastercard works with other financial institutions and utilises AI to provide them with customised solutions. Like most other financial companies, AI is used in optimisation and as a result cuts costs for both customers and the company. Prioritising customer centricity and user experience, Mastercard uses anonymised transaction data to help customers make better decisions.

As provides the platform for companies to leverage AI and machine learning, Parul elaborates that Hybrid Cloud provides state-of-the-art results but does not compromise on the explainability or accuracy. helps customers as they compete with one another in the digital arena.

Gender Disparities within the Field

It’s no secret that the fields of science and technology have always been male-dominated and still are to this day. Although there are no obvious barriers to women entering those fields, decades of societal stereotypes have implanted biased ideologies that males naturally perform better than females in STEM subjects. These unconscious biases have led to sexism within education and the workforce, creating an undesirable environment for women. Making them feel unwanted, pushing girls into degrees within the social sciences, biology, psychology, and arts.

Although there have been studies to show that on average females are more dominantly right-brained, all three panellists agreed that it does not have an adverse effect on their abilities to contribute to the field of data science. Parul states, “I’ve competed with the boys all my life and I’ve done well. If you’re passionate about this field, it doesn’t matter if you’re left or right-brained. Data science is a diverse field, and it requires people from all backgrounds.” To back that up, Seema notes that from her observations, since data science deals with pattern matching and visualising, the female members of her team can look at data sets in a more creative way, which is useful.

In response to the notion that women are not suited for the field of technology, Seema emphasises, “Women have to be confident and passionate about the field to have a better, constructive role in it.” The three women on the panel are great examples of women’s potential in the field of data science. As technology develops and the world progresses, more and more initiatives have been taken by organisations and governments worldwide to encourage girls to participate in science and technology. One such organisation is Girls in Tech Singapore which is dedicated to eliminating the gender gap in technology.

The panellists also shared some advice they had for women looking into joining the field of data science, stating that one of the most important qualities to have is the willingness to learn. They agreed that nowadays, it’s much easier for people to have access to education as everything has been made available online. It doesn’t matter the stage of life you’re currently at, there is still time to learn. But before jumping into this field, women should first truly understand the field and ensure their interest in it isn’t just because of the current hype surrounding AI. For graduates looking to make it in the working world, they should use different platforms to get connected to people within the industry and try to gain some real-life experience. Although the ratio of men to women in science and technology is not optimal, some progress is being made with more females enrolling in science and technology courses in university.

AI biases don’t only occur on a gender basis but have been shown to also cause problems with people from minority ethnic backgrounds. Facial recognition especially has gotten some innocent racial minorities in hot water with the authorities due to misidentifications. In a sense, the programming of AI technology has brought the underlying prejudices present within society to the surface. To address this, Parul highlights that while people are talking about the AI biases that are present, not enough is being said about creating systems to overcome these biases. She goes on to say, “Organisations like to think of AI as a magic wand that can be sprinkled at the end. AI has to [be] imbued in your product and has to start from the stage of planning”, urging organisations to look back and ask themselves whether their data is based. Team diversity plays a major role in this as a team consisting of people from different backgrounds, education, gender, and races can easily pinpoint biases at the beginning of the process.