State space models in credit and operational risk management

This project targets development of applied methods and practical solutions to risk management problems where only partial observation of a system is possible. Such settings are commonplace in financial and other context but can be challenging to address due to a limited number of production-grade ready-to-use solutions. The scientific component of the project employs approaches from a quickly developing and active area in machine learning. More extensive use of these approaches by Canadian banking institutions will lead to a more robust financial system and better service.

Improving the Accuracy of Data Loss Prevention Systems

Scotiabank employs teams of cybersecurity specialists across its global operations and partners with a variety of external organizations to prevent and investigate any electronic attempts to gain access to the Bank’s data. At the same time, employees are continuously educated and expected to look for warning signs and efforts to infiltrate that data as well. Currently, Scotiabank’s Data Loss Prevention (DLP) systems have a high false positive rate in identifying data breaches and cyber-attacks, which require significant manual intervention.

Applications of Blockchain for Cross-Border Deposits in the Finance and Banking Sector

Blockchain technology changed how e-payments work and opened the door for development of advanced and secure e-payment systems. Currently, cryptocurrency is the only well-known, successful application of blockchain technology. However, the application of blockchain technology is not limited to cryptocurrencies. Many distributed ledger technology experts discussed the benefits of applying blockchain in many sectors such as, finance, government, healthcare, energy, supply chain, and transportation. One important sector expected to benefit significantly from blockchain is the banking sector.

Customer Lifetime Value Prediction Engine: Retail Lending Products

Canada’s financial services industry faces significant challenges to remain internationally competitive in the rapidly evolving web and big data environments. Scotiabank and its global competitors have as a key priority effective use of a large and growing amount of data to optimize the design and pricing of product offerings, to communicate effectively with clients, and to mitigate risk.

A behavioural risk model for deposit only customers

In this joint collaboration with BNS, we will develop a behavioural risk model to predict the likelihood of future risk of breaking the promise to pay debt for customers who only hold deposit products with BNS. The model will be utilized to support business operations such as credit card and loan pre-approvals. That is to say, if you are a customer who only have chequing, saving and/or investment accounts with BNS and plan to buy a car, you will be scored in this model for the car loan pre-approval.

Customer Lifetime Value Prediction Engine: Neighborhood Link Inference and Conversion Prediction

Canada’s financial services industry faces significant challenges to remain internationally competitive in the rapidly evolving web and big data environments. Scotiabank and its global competitors have as a key priority effective use of a large and growing amount of data to optimize the design and pricing of product offerings, to communicate effectively with clients, and to mitigate risk.

Anomaly Detection in Financial Data

In this joint collaboration with Scotiabank we hope to solve a commonly faced problem by large financial institutions. It is to detect errors in financial datasets. This could be due to typing errors made by a human or a computer glitch that causes an incorrect value to be stored. To identify these errors, we plan to build an error detection system. It will model how financial variables change in relation to other variables. This will help us identify groups of variables that move, through time, in a similar manner. With this knowledge we will then be able to spot errors in the data.

Comparative Analysis on Various Blockchain Technologies and How Can They Transform the Financial Services for Scotiabank

Blockchain is an emerging technology that has the potential to change the way financial participants transact with each other. It enables direct transfer of value and financial assets between participants over networks without the need for a central authority (internet of value). It does this by combining the functionality of different technologies - distributed systems, smart contracts, mutual consensus verification, and cryptography. Given its potential Scotiabank is investing in technical research and business application.

Group IT usage, diversity and innovation

This proposal is part of a larger research program that aims to study the relationships between IT usage for communication, diversity in social and technical knowledge, and innovation in groups. We have developed a research framework based on social network theories to suggest two different mechanisms for relating diversity and IT usage to novel idea generation and implementation. Our findings will help the partner organization to improve its capability to innovate by better forming and managing diversity within and across group in the context of innovations labs.

Customer Lifetime Value Framework for the Banking Industry

The Customer Lifetime Value (CLV) framework provides a holistic approach to measurement and management of long-terms customer relations. The goal is to take into account all of the services and products customers need or might need across an organization and maximize long term benefits for both the firm and the customer. While simple in concept, CLV is not simple to implement.