In Canada, financial advisors and dealers are required by provincial securities commissions and self-regulatory organizations--charged with direct regulation over investment dealers and mutual fund dealers--to respectively collect and maintain Know Your Client (KYC) information, such as their age or risk tolerance, for investor accounts. With this information, investors, under their advisor's guidance, make decisions on their investments which are presumed to be beneficial to their investment goals. Our unique dataset is provided by a financial investment dealer with over 50,000 accounts for over 23,000 clients. We use a modified behavioural finance recency, frequency, monetary model for engineering features that quantify investor behaviours, and machine learning clustering algorithms to find groups of investors that behave similarly. We show that the KYC information collected does not explain client behaviours, whereas trade and transaction frequency and volume are most informative. We believe the results shown herein encourage financial regulators and advisors to use more advanced metrics to better understand and predict investor behaviours.
Disruption ahead: Deloitte's point of view on IBM Watson8 9. What makes Watson unique In technical terms, IBM Watson is an advanced open-domain question answering (QA) system with deep natural language processing (NLP) capabilities. At this point, the Watson Software as a Service (SaaS) platform is most effectively used to sift through massive amounts of text--documents, emails, social posts, and more--to answer questions in real time. Watson accepts questions posed by the user in natural language and provides the user with a response (or a set of responses) by generating and evaluating various hypotheses around different interpretations of the question and possible answers to it. Unlike keyword-based search engines, which simply retrieve relevant documents, Watson gleans context from the question to provide the user with precise and relevant answers, along with confidence ratings and supporting evidence. Its learning capabilities allow Watson to adapt and improve hypothesis generation and evaluation processes over time through interactions with users. Developers and other users can improve the accuracy of responses by "training" Watson. IBM is also continuing to expand Watson's capabilities to incorporate visualization, reasoning, ability to relate to users, and deeper exploration to gain a broader understanding of the information content. Watson recently launched a new platform service that has the ability to ingest and interpret still and video images, which is another significant type of unstructured data.
While the impact of artificial intelligence (AI) is a bit of a mixed bag in a number of industries, we're seeing some exciting traction in financial services. In this month's article, I take a look at some specific examples of where machine learning and AI are helping financial services organizations improve their services, products, and processes. Financial firms and banks are taking advantage of AI to ensure that their employees are meeting complex disclosure requirements. Generally, financial advisors must make sure that their "client advice" documents include proper disclosures to demonstrate that they're working in their client's best interests. These disclosures may cover conflicts of interest, commission structure, cost of credit, own-product recommendations and more.
After being formally announced in December 2017, the Royal Commission into Misconduct in the Banking, Superannuation, and Financial Services Industry has released its final report, which provides a solution that proposes to disallow a repeat of the many cases of misconduct the probe uncovered; the real-time sharing of information between two of Australia's regulators. The 530-page, Volume 1 report [PDF], signed off by Commissioner Kenneth Hayne made a total of 76 recommendations, with one requesting the Australian Securities and Investments Commission (ASIC) and the Australian Prudential Regulation Authority (APRA) be required to share information via a database that both parties have access to. Hayne suggested the scheme be founded on the premise that "joint responsibility and co-operation necessitates substantial commonality of information". "I favour a model that prefers mandatory, rather than discretionary, sharing of information. ASIC and APRA should, to the greatest extent possible, work from a single body of relevant information," he wrote.