In the 2022-23 academic year, the CEFAR Academy has secured 57 projects from 30 sponsor organizations. Under the “Fintech 2025” strategy, Hong Kong Monetary Authority (HKMA) has launched the Industry Project Masters Network (IPMN) scheme with the aim of grooming fintech talents by providing opportunities to postgraduate students to work on banks’ fintech or industry projects and gain hands-on experience and skills. Out of the 57 projects secured in 2022-23 academic year, 24 are offered via HKMA IPMN scheme, soliciting the support from 17 sponsor organizations.
The scope of the applied research projects are categorized by Technology Domains and Business Domains respectively as follows:
In terms of Business Domains, the scope of the applied research projects are categorized as follows:
Completed Projects in 2022-23
With increasing ecological concerns, the finance sector is shifting towards sustainable lending. This project introduces a green financing system that offers lower loan rates to businesses meeting ESG (Environmental, Social, and Governance) benchmarks. Utilizing real-time environmental performance data from APIs like those from HK Electric, the system aims to financially motivate companies to pursue greener practices.
To advance fintech innovation and enhance the efficiency of financial operations, Hong Kong could create a central KYC database. This facility would supply authenticated digital identities for people and companies, streamlining the document and verification demands on financial firms. This system aims to reduce the effort and duration dedicated to KYC/AML tasks, thereby quickening the delivery of services and raising compliance levels.
Banks are poised to embrace a transformative shift by embedding artificial intelligence (AI) and machine learning (ML) into their foreign exchange (FX) trading functions. This shift aims to minimize the current dependency on manual labor by introducing automated systems for tasks like algorithmic trading, enhancing risk management, and efficiently managing numerous small-scale FX trades. The integration of AI and ML technologies is expected to streamline banking workflows, elevate operational efficiency, and modernize the conventional approach to FX trading.
UNCLE2 is innovating the pawn industry by introducing a platform for tokenizing luxury assets. With its AI-based pawn matching and auction system as a foundation, the company seeks to convert tangible luxury items into digital tokens on the blockchain. This move will facilitate shared ownership, making investment in luxury assets more accessible to a wider audience and increasing the ease of trading these high-value items.
This project capitalizes on data from 500,000+ smart home devices, aiming to improve user experience and energy efficiency using AI and IoT. The focus is on creating AI algorithms to predict energy-saving settings, implementing real-time cloud data processing, and developing a recommendation system for smart configurations. The end goal is to boost comfort, reduce costs, and introduce innovative smart home services.
This project uses data from 500,000+ smart home devices to improve user experience and detect anomalies. By analyzing patterns and behaviors, it identifies irregularities like unusual energy consumption and abnormal device behavior. Real-time detection and periodic analysis enable proactive alerts and automated actions, enhancing device performance. The collected data also aids in fraud detection, uncovering unauthorized access attempts and fraudulent behavior. Leveraging the vast amount of smart home device data, this project aims to create a secure and reliable environment while improving user experience through anomaly detection and fraud prevention.
Citibank HK is investigating the deployment of an AI chatbot to boost the productivity and client contentment of its Citiphone service division. This project is tasked with researching and suggesting a chatbot system capable of handling routine customer questions and potentially complex transactional requests in the future. Studentst will conduct an in-depth review of what customers anticipate, identify crucial banking functions for chatbot deployment, evaluate providers of chatbot solutions that accommodate English and Chinese languages (both Simplified and Traditional), and establish KPIs to track the success of the chatbot implementation.
ALGOGENE is leading innovation in the financial sector by creating a user-friendly platform that empowers retail investors to design their own trading bots. The goal is to simplify entry into algorithmic trading through straightforward tools and educational content that align with individual risk preferences and investment targets, while also complying with regulatory norms.
NFTs represent a groundbreaking development in blockchain technology with the capacity to transform concepts of digital ownership and verification. Their unique, non-duplicable attributes are well-suited for the digital representation of tangible assets, potentially streamlining and securing transactions. This project will delve into the ways NFTs could boost trade finance operations and will identify actionable finance and trade use cases for HSBC. Students will focus on the integration of NFTs with existing financial infrastructures, their potential to shake up conventional markets, and the overall consequences for international trade dynamics.
Smart contracts, the automated transactions of blockchain technology, are susceptible to security threats due to their fixed and visible characteristics, resulting in losses of over $1 billion. This project seeks to address these security issues by educating students on the core concepts of blockchain, the design of smart contracts, and skills in platforms like Ethereum. The students will investigate possible attack strategies, analyze the exploitation of flaws, and research defensive tactics to reinforce the security of smart contracts and preserve the integrity of the blockchain network.
This research explores the retail applications of Central Bank Digital Currencies (CBDCs) in Hong Kong, following the direction set by the HKMA's "Fintech 2025 strategy". The aim is to discover effective use cases for CBDCs in routine transactions and develop an adoption strategy for digital currencies like e-CNY and e-HKD, specifically for local and cross-border payments within the Hong Kong environment.
Public cloud services deliver scalable computing resources, but they also introduce notable risks to data security and privacy that may deter their use. Firms moving to the cloud can lose immediate control over their data, encountering difficulties with managing access permissions and encryption keys. Data confidentiality, integrity, availability and auditability are important factors to maintain. This project will focus on these essential needs, striving to identify and alleviate the potential dangers linked to public cloud usage in order to strengthen the protection of data.
The project investigates the integration of privacy-preserving computation technologies into cross-border payment systems, which are crucial yet often complicated by diverse regulatory requirements related to data privacy. The project will assess the application of these technologies in global payment platforms, with an emphasis on improving payment channel optimization, routing, and risk control. The goal is to establish how these technologies can enhance the efficiency, security, and regulatory compliance of international financial transactions.
In this era where protecting data privacy and security is crucial, establishing a secure framework for instantaneous data sharing among multiple stakeholders is essential. This project is dedicated to constructing a decentralized network that sorts data into public, shared, and private tiers. The objective is to ensure the safe distribution of 'shared' data assets, safeguarding confidential information and impeding unauthorized changes. The framework will integrate a detailed audit log for tracking all alterations and implement strong security controls to detect any purposeful actions, thereby preserving the integrity and privacy of the shared data assets.
The goal of this project is to refine customer insights and more precisely shape product offerings by enriching current customer data with information from external and third-party sources. Utilizing publicly accessible data from platforms like social media and oversight organizations in Hong Kong and China, along with internal data evaluations, the project seeks to expand the understanding of customer demographics. Additionally, it involves exploring new data providers to better understand the needs of corporate clients, ultimately refining product strategy and development.
BankComm aims to utilize sentiment analysis, web scraping, and natural language processing (NLP) to monitor its brand perception and gather insights on product reception. The aim of the project is to develop a branding index tailored to the industry while capturing detailed information on customer experiences and sentiments. By evaluating customer opinions and measuring up against the industry's top virtual banks, BankComm seeks to adjust its marketing strategies to attract the modern customer base and pinpoint new opportunities for interaction and engagement.
With the HKMA advocating for the use of Commercial Data Interchange (CDI) and emphasizing sustainable finance, BEA is looking to upgrade its financial models by integrating non-traditional and ESG (Environmental, Social, and Governance) data. This integration is targeted at improving the bank's strategies for investment and risk management across different sectors, including retail and corporate finance, insurance, and asset management. The project will explore the capacity of such data to yield deeper analytical insights, foster investment decisions that are socially responsible, and bolster risk management efficacy within BEA's operations.
Equity-Linked Investments (ELIs) are widely offered by banks to diverse customers. In the face of digital growth and more investment options, banks need to innovate to retain ELI investors. This project will review the ELI sector, assessing product range, digital engagement, and marketing effectiveness. It seeks to identify what influences customer loyalty to ELIs and aims to develop a tool that tracks ELI transactions, market trends, and customer behavior to provide personalized portfolio and marketing recommendations.
Tailoring customer service in finance relies on an accurate understanding of a client's total assets. For the purpose of assigning knowledgeable relationship managers to affluent clients and customizing high-end product options, predictive models are needed to assess a customer's external Assets Under Management (AUM). This project is focused on building a sophisticated model that combines internal customer behavior data with external data sources to accurately predict a customer's complete wealth profile, including AUM not held at the bank. This will help the bank offer more individualized and effective customer service.
The pandemic has increased e-commerce growth in Hong Kong, changing shopping habits and boosting online sales. Fintech is expected to further fuel this expansion by offering smooth payment options, merging banking services, and making financing more available. This project will examine how Fintech can simplify entry, enhance transactions, and support Hong Kong's e-commerce ecosystem.
Hyper-personalization is reshaping E-commerce by providing customized user experiences through the integration of data, analytics, artificial intelligence, and automation. A crucial element in this shift is the use of Multimodal Knowledge Graphs (MMKGs), which amalgamate different data types, including images, text, and videos. This project aims to delve into the creation of a high-performance MMKG from readily accessible open data sources and its implementation in enhancing recommendation engines. Students will focus on the techniques for acquiring, purifying, and harmonizing multimodal data, the tactics for consolidating this data into a single, comprehensive knowledge graph, and the strategies for employing MMKGs to deliver more targeted and efficient product recommendations.
The incidence of financial fraud is increasing, with complex methods such as impersonation and deceptive banking messages leading to significant financial loss and undermining confidence in the banking system. Banks are countering this with more sophisticated fraud detection systems across their operations. This project aims to investigate the most widespread forms of financial fraud and identify the banking channels they commonly exploit. Students will explore the ways in which banks can apply technological advances and strategic approaches to effectively combat these threats. Students will engage in developing protective measures that ensure customer security and uphold the banking infrastructure's integrity.
This project aims to build a machine learning (ML)-enhanced search engine to better detect financial fraud, learning from human reviews to improve its accuracy and reduce false positives. Students will create a system that not only fetches relevant data with keyword searches but also uses user feedback to refine results. The engine will eventually assign confidence ratings to its findings, simplifying the detection process and lessening the reliance on manual checks.
The project is focused on developing a language model that accurately maps medical diagnoses to ICD 9/10 codes, aimed at automating claims and underwriting for Hong Kong's insurers. It seeks to surpass existing solutions like Amazon Medical Comprehend with a localized model that integrates historical claims, Hong Kong Hospital Authority records, and data from the Chinese University of Hong Kong's hospital, ensuring it meets regional data regulations and improves ICD coding accuracy for the Hong Kong healthcare context..
The insurance industry is evolving to focus on "Prevention" rather than just "Protection," with premiums adjusted based on personal health metrics and lifestyle behaviors. The objective is to devise a Machine Learning model that can project future health conditions by evaluating IoT data, family health history, and lifestyle choices. This method will allow for the determination of personalized, fair insurance premiums. The challenge lies in developing a model that is specifically suited to the unique medical framework of Hong City, where currently no appropriate pre-built solutions are available.
In the domain of financial trading, the scattered nature of counterparty positions across various entities holds key insights. The project aims to employ machine learning to combine complex, multi-source data to discern patterns of market risk. Utilizing techniques such as clustering algorithms, NLP, and neural networks, the initiative aims to gain deeper understanding of company conduct and associated market risks. The expected outcome is to build a model that can automate daily analytical tasks to enhance the quality of trading decisions.
Financial entities are crowded with physical documents, from client files to fiscal reports, leading to a bottleneck in data processing and access. The challenge is to move from paper to digital records in an efficient manner. This project aims to develop an Optical Character Recognition (OCR) system that is adept at recognizing Chinese script, allowing for the transformation of hard copy documents into searchable digital formats. This conversion is key to simplifying data management and boosting the efficiency of handling financial information.
The project sets out to solve the problem of scaling the integration of diverse SaaS applications for SMEs, focusing especially on automating processes through the use of API links, including those with banking systems. The objective is to make the incorporation of various SaaS tools more efficient and to enable straightforward self-service integrations. The plan involves offering assistance to SaaS developers in connecting with bank APIs and streamlining the process for them to establish their profiles on our platform, which will allow for seamless integration of third-party SaaS offerings at a broad scale.
The fusion of Robotic Process Automation (RPA) and Artificial Intelligence (AI), known as Intelligent Automation, is transforming the regulatory technology (RegTech) field, especially in the banking, financial services, and insurance (BFSI) industry. This project is dedicated to exploring and creating cutting-edge solutions to improve the processes of Know Your Customer (KYC) and Anti-Money Laundering (AML). By employing technologies like unstructured data analysis, analytics for reporting, Natural Language Processing (NLP), RPA, and Optical Character Recognition (OCR), the goal is to substantially boost the efficiency and precision of Suspicious Transaction Reporting (STR) within banking institutions.
The pandemic has decreased physical interactions for Wealth and Estate Management (WEM), necessitating a remote, compliant, and engaging sales process. This project will investigate fintech solutions to enhance the remote WEM sales experience, focusing on shortening the sales cycle, ensuring compliance, automating verification processes, and remote identity checks. The goal is to establish a streamlined, secure remote WEM service that meets modern customer needs.
CG Global Entertainment Ltd. is gearing up to transform the digital media space by building an ecosystem that allows creators to profit from their intellectual property via NFTs. Addressing the economic hurdles that SMEs in Hong Kong face, such as expensive creative software subscriptions and high fees on blockchain platforms, CGGE plans to introduce a holistic, wallet-friendly platform. This platform will streamline the process of producing and selling digital assets, cutting down the expenses linked to minting and trading NFTs, and broadening the reach of the NFT market to include more creators.
Digital wallets have reshaped how consumers manage transactions, offering commercial banks a chance to tap into this trend. This study will assess the market for digital wallets in commercial banking, identifying key applications and trends. It aims to position commercial banks strategically within the digital wallet sector by examining integration with existing services and the impact on banking operations.
The project sets out to tackle the problem of customers dealing with a scattered financial landscape by developing an integrated tool that brings together various financial products, including investments, MPF accounts, and different bank accounts, into one cohesive platform. This system aims to present a complete picture of a customer's financial assets and offer customized advice to improve customer engagement. The platform is intended to be expandable, with future capabilities to include the management of insurance policies and other financial offerings.
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