On the 27th October, JurisTech had the pleasure of contributing to Universiti Malaya’s (UM) AI Day. See Wai Hun, our respected CEO, was graciously invited to share insights on the practical applications of artificial intelligence (AI) in the financial and fintech sectors. 

Her presentation drew a diverse audience of more than 30 students from UM’s Faculty of Computer Science and Information Technology.

Wai Hun giving a welcoming address to UM students during her presentation

Wai Hun introduced a captivating activity to start the conversation. To encourage excitement and participation, she engaged students in a number of pop-up quizzes to which they can win some exciting prizes. Following that, she gave an overview of JurisTech’s history, emphasising the company’s provision of software solutions to banks, financial institutions, telecommunications companies, and luxury vehicle companies, thereby contributing to economic advancement.

In addressing the critical role of small and medium enterprises (SMEs) in economic development, Wai Hun stated that an astounding 97.2% of business establishments in Malaysia fall into this category. These SMEs, which number 1.15 million nationwide, account for more than 38% of Malaysia’s GDP, exceeding RM 500 billion, and employ nearly 70% of the workforce. Given the significant impact of SMEs on Malaysia’s economic landscape, JurisTech’s goal is to reshape the financial industry by providing software solutions that promote financial accessibility and stimulate economic growth.

The Forecast for AI

Gartner’s Emerging Tech Impact Radar for 2023 — Artificial Intelligence

Wai Hun displayed Gartner’s Emerging Tech Impact Radar for 2023 to the students (shown above). This diagram highlights the technologies and trends that have the potential to revolutionise various markets, providing product leaders with strategic insights into market opportunities. Through this, Wai Hun stressed the importance of Generative AI, Composite AI, and Citizen Data Engineer.

Generative AI

Wai Hun then went into more detail about generative AI, which is a type of AI that can generate new content, such as images, videos, or text, by leveraging patterns learned from existing data. While this capability is remarkable, generative AI is not without risks and benefits. 

One notable advantage of generative AI is its ability to generate new and innovative products and services, such as tailored financial advice or customised investment portfolios. On the other hand, there is a potential disadvantage: the occurrence of hallucinations. In the context of generative AI, hallucination refers to the creation of false or non-realistic content. An example of this was seen when ChatGPT was criticised for inadvertently including fictitious legal research in a court filing.

Composite AI

Wai Hun defined composite AI as the integration of various approaches such as artificial intelligence, heuristics, human judgement, and statistics.

The combination of these techniques improves learning efficiency and develops more advanced knowledge representation, allowing composite AI to solve a broader range of business problems.

Applications of AI in the Banking Industry

Wai Hun then provided several examples of how artificial intelligence is being used in the banking industry.

Digital Onboarding

AI is used to streamline the process of onboarding new customers, resulting in a faster and more efficient experience. This includes e-KYC processes, personalisation, and strategies for cross-selling and upselling.

Loan Origination

Using AI speeds up loan disbursements by streamlining document processes and incorporating prescriptive analytics. This has the potential to reduce loan disbursement times from weeks to as little as 15 minutes.

Debt Collection

Through AI analytics, AI is used to develop personalised and automated collection strategies, as well as to identify non-performing loans and self-rectifying accounts. This can be crucial in improving bank collection rates and lowering the number of delinquent accounts.

Big Data in Banks

Wai Hun highlighted that banks possess an abundance of data, as seen below. 

The different kinds of data that banks can utilise for better service personalisation.

Using this data allows for a better understanding of customer actions, trend recognition, and operational efficiencies. However, fully utilising its value necessitates advanced analytical capabilities and a robust data infrastructure.

Unleashing The Data

Wai Hun then revealed that there is a way for banks to make use of this treasure trove of data by combining ModelOps and autoML. 

ModelOps

ModelOps plays a critical role in enterprise AI by simplifying model deployment and management in a live environment. This includes developing a standardised protocol for model deployment and management, as well as implementing best practises for ongoing monitoring and maintenance. Organisations can improve the efficiency and efficacy of their AI and data science efforts by implementing ModelOps, while also reducing the risk of errors and system downtime.

AutoML

AutoML is a set of tools and methods for automating the creation, training, and deployment of machine learning models. It aims to simplify the machine learning model development process by automating numerous laborious and intricate tasks such as feature engineering, model selection, and hyperparameter tuning. This can help organisations build machine learning models that are more precise and efficient, all in a shorter period of time and with less resource allocation.

Why ModelOps and autoML?

AI projects require multiple teams of experts. These teams typically include data scientists, machine learning engineers, software developers, domain experts, and project managers. Each team member’s unique skill sets and proficiencies contribute to the project’s success, emphasising the importance of collaborative efforts. Furthermore, AI projects necessitate access to high-quality data, powerful computing resources, and advanced analytics tools. 

AI projects also require complex processes. Defining the problem, gathering and extracting data, pre-processing the data, structuring the dataset, selecting and tuning machine learning models, assessing model performance, deploying the model, and ongoing monitoring and management of the model are all part of this process. These procedures all have several steps and call for a high degree of skill and precision. Another level of complexity is added to the process by the fact that AI projects call for ongoing learning and adaptation to shifting conditions.

These processes are streamlined through the use of ModelOps and autoML, which employ continuous learning.  

How Continuous Learning in AI Works

Enter Citizen Data Engineer

Wai Hun pointed out that ModelOps and autoML have paved the way for the emergence of Citizen Data Engineers. For several reasons, this trend is gaining traction. One of them is the demand for domain experts in businesses. While IT data engineers may be technically proficient, they may lack the business domain knowledge required for accurate data curation.

Moreover, there is currently a shortage of expert data engineers, and the demand for scalable and reliable data systems is high. Citizen data engineers are emerging to meet this demand and bridge the knowledge gap between technical expertise and business domain knowledge. 

Finally, the democratisation of AI is influencing the demand for domain experts, as more people want to use AI in their businesses but lack the necessary technical knowledge.

Wrapping Things Up

As the session came to a close, Vechrani, JurisTech’s senior recruiter, discussed the exciting career opportunities available to software engineers and business analysts at JurisTech. She emphasised how software engineers can create both client and server software, demonstrating their versatility. This, combined with their constant improvement of knowledge and design skills across a range of fields, including databases, front-end, and back-end, leads to the creation of software solutions that provide significant value to customers. 

Vechrani also introduced the Heroes Training Academy (HTA), a unit designed for new recruits with no prior tech skills, offering them a pathway to embark on a career as Business Analysts. In a comprehensive two-week training, HTA equips participants with the vital skills required to succeed in the Fintech industry.

Wai Hun receiving a token of appreciation for UM representatives

At the end of the session, UM invited Wai Hun to the stage to accept a token of appreciation for the insightful talk. JurisTech’s representatives were then cordially invited by UM representatives to a delicious lunch that included a variety of curried dishes. The audience was inspired and motivated by the variety of opportunities provided by JurisTech, as evidenced by their eagerness to stay after the talk to ask Wai Hun and Vechrani questions about the company.

About JurisTech

JurisTech (Juris Technologies) is a leading fintech company, specialising in enterprise-class software solutions for banks, financial institutions, and telecommunications companies in Malaysia, Southeast Asia, and beyond.