Why Business Owners May Not Need an RRSP

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At this time of year, RRSPs are top of mind for a lot of people. The February 29th deadline is hardwired into us as Canadians and many are scrambling to make contributions before that time. Despite the introduction of TFSAs, RRSPs are still the most popular savings vehicle for Canadians.

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Financial Asset Management Systems, Inc. (FAMS)

Financial Asset Management Systems, Inc. (FAMS) is an Account Recovery Solutions provider that has developed an industry-leading, multi-tiered approach to loss prevention, account rehabilitation, and revenue recovery.

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How Exchange 4.0 will Digitally Transform Financial Market Infrastructure

Article | August 17, 2021

Powered by Ledgers: Leading Market Experts Predict How Exchange 4.0 will Digitally Transform Financial Market Infrastructure The move to Exchange 4.0 is well underway, with profound implications for financial markets. Forward-thinking firms are already positioning themselves for a DLT-fuelled future. But behind the buzzwords, there are lingering questions. What benefits will digitalisation bring, both to trading venues and the market participants they serve? What are the main obstacles to Exchange 4.0, whether they stem from outdated thinking or misaligned stakeholder incentives? And what sort of step-changes can we expect as digitalisation takes off? In a recent report, Hirander Misra, Chairman and CEO of GMEX Group, and the Realization Group interviewed experts at firms pioneering the new world of crypto asset trading Alokik Advani, Managing Partner, Fidelity International Strategic Ventures Charles Kerrigan, Partner, CMS London Jessica Naga, Director Responsible for Legal and Compliance, SECDEX Anoop Nannra, Global Blockchain Segment Leader, Amazon Web Services Nicholas Philpott, Director, Zodia Duncan Trenholme, Head of Digital Assets, TP ICAP. We summarise the key highlights and perspectives from virtually every stakeholder group involved in the trend towards digitalisation. Introducing Exchange 4.0 Just as the world is experiencing a fourth industrial revolution, sometimes called 4IR, financial exchanges are beginning their own technological revolution. The 4IR concept is the driving force behind the Internet of Things, where AI and web technology combine to create smart products. A similar idea is taking hold in the world of financial market infrastructure enabled exchange trading, as DLT, smart contracts and tokenisation make it possible to facilitate true asset portability while linking far-flung liquidity centers. But there is a great deal of confusion as to how distributed technology will change financial market infrastructure so that it can make the transition, be fit for purpose and what benefits it will bring. There are also significant roadblocks, either in terms of old-fashioned thinking or stakeholders defending their turf. Experts say it is only a matter of time before these obstacles are overcome. The first step, they say, will involve trading venues and participants developing a new mindset, one that embraces open-source practices. As Exchange 4.0 becomes better understood, and as firms move from proof of concept to bottom-line benefits, we can expect a rash of major changes. New trading centers, new products, new ways of doing business and new ways of enabling post trade are all on the way. Creating the network effect A growing number of exchanges and trading firms are embracing distributed ledger technology (DLT) and tokenisation, recognising a surge of interest in crypto asset trading from both retail and institutional investors. But many of the venues are replicating silo-based models and missing out on the most important lessons from the digital revolution. DLT, tokenisation and crypto asset trading offer a chance to create much larger market ecosystems by enabling participants to transact across borders more easily and by facilitating asset portability. Rather than divvying up the pie, it’s all about making the pie much larger. “The key thing about this is asset portability,” says Hirander Misra. “If you look at marketplaces in this space, there are lots of exchanges across the world and there’s tumbleweed growing through most of them. How do you create that network effect? But then also, how do you focus on what you’re really good at?” Misra says the problem starts with exchanges adopting a silo mentality, where they seek to service clients exclusively rather than building a more collaborative model. Trading, clearing and settlement end up being offered in a closed-in environment. “Essentially these exchanges are just pockets of their own liquidity.” But the future could soon look very different. “You’re going to see exchanges, custodians and other services interconnect more seamlessly, with the ability to swap services and assets across jurisdictions and across different types of users to get that network effect. This is a construct that I have labelled Exchange 4.0,” Misra says. What the Experts Expect Provided that network effect can be created, what sort of benefits can firms look forward to? The list is long and varied. Alokik Advani:“You have to try this in pockets of smaller assets, where it can be really efficient – private markets, alternative assets, private equity, venture capital, real estate, private debt. All of these things are obscenely inefficient. They trade like bulletin boards today. If you wanted to bring that to some level of an exchange-like infrastructure with a DLT backing and speed of clearing and settlement, it’s a revolution.” Charles Kerrigan: “You are seeing the move towards digitalisation as a prime example of capitalism forcing change. You are talking about another wave of creative destruction. We have digitalised the front office of financial institutions – what you see as a customer – but the real benefits will come from digitalising the market infrastructure. Crypto shows how this can be done. Payments have learnt from that. Securities issuance is following. We are simply following the logic of the information economy. This is a big one.” Hirander Misra: “With Exchange 4.0, say you’re an existing exchange and you have existing infrastructure. You may want to set up a digital exchange, but you may not want to replicate everything you have. You may not need another matching engine, you may need digital custody or you may need issuance. The thing about Exchange 4.0 is that you can combine the services you have with services others have or augment what you already have. So, you’re not beholden to creating yet another siloed infrastructure.” Jessica Naga: “There is something to be said for the countries that take the jump and do this now fast. They will have first movers’ advantage, if they build the necessary legal framework and infrastructural ecosystem in a sustainable way. The clear advantage of technology and FinTech companies is that their business is cross border and therefore from one centre, they can service the world.” Anoop Nannra: “We look at Exchange 4.0 and the opportunities in terms of creating digital assets on virtually any aspect of our business. I think it’s really exciting, being able to create a futures index based on real-time solar energy production. Right down to the second. You create new patterns and opportunities for liquidity to occur. Capital historically will move to the environments where liquidity is most easily had.” Nicholas Philpott,: “The locations and the cities that succeed in the future may no longer be the same as the ones at present. It’s a much more even competition now. If you can spin up a virtual exchange with none of that physical infrastructure that opens up the possibility of some very interesting developments as far as the new trading centres of the future are concerned. You’re broadening the market across a bigger spectrum of participants. More people can have access.” Duncan Trenholme: “It’s possible that some of the private permissioned blockchains get traction in certain areas and solve certain use cases, but over time we believe the open permission-less blockchains will eat market share. The idea of running your own distributed ledger, in a centralised manner, just misses the point of what this technology can do. It’s repeating the limitations of vertical silo’s all over again. As people do connect, they’ll increasingly experience the benefits of transacting on an open, interoperable, and programmable financial system.” A way forward All of this leaves traditional venues and market participants having to prepare for a wholesale change in the way they operate while still conducting business in the here and now. At the same time, scores of new exchanges have sprouted up with DLT technology and digital assets that can only be traded on one platform. By forging the DLT-based world of the future while still servicing traditional assets in traditional ways, we will see a hybrid model which bridges the gap between digital and traditional financial market infrastructure. This will serve to eradicate the current silos and fragmentation to facilitate better portability of assets by interconnecting the whole capital markets value chain of participants, across international nodes (jurisdictions), to more easily trade, clear and settle.

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FINANCIAL MANAGEMENT

Technological Innovation and Everyday Lifestyle

Article | August 17, 2021

Technological innovation over the recent decades has attracted increased interest of researchers and industry experts thereby becoming an integral part of everyday lifestyle. The challenge lies in resolving the dispute between a forward-looking innovative structure promoting innovation, and a proportionately meticulous schema that is capable of winning faith of consumer. Information technology is fast turning to be a key instrument in the lives of consumer across generations. In current global economy, customers across industries have been pampered and the credit goes to “Bigtechs ” like Ali Baba, Apple, Amazon, E-bay, the list being exhaustive along with “Fintech ” and “Incumbent banks ” as a result, consumers expect swift product delivery with flawless service. A review of available literature is suggestive that further expositions tend to focus on fintech and its integration in banking arena investigating the factors that underpin the choice of external partners to collaborate, design, develop and implement fintech capability while addressing the gap between research and industry evolution. Recent developments in technology have refurbished global economies at an immensely fast pace, making the business environment extremely challenging with continued margin pressure. Digital technologies are intrusive to not only the competition but also to the role of payments in businesses impacting the ultimate consumers. Investigating digital transformation has been of continuing interest across industries. Digitization might abolish some vital job roles, threatening the human workforce reluctant to digital changes. However, observations are indicative of focus towards higher-value tasks and creating unprecedented opportunities. For instance, adoption of digitization in financial industry, provides considerable opportunity to relationship managers to spend minimal time in operational activities and maximum towards advising customers. Amongst many ideas laying the foundation of fintech adoption, a growing body of literature recognizes two vital causes for the evolution of fintech companies that can be routed back to a decade. Firstly, the global economic financial crisis also called economic recession that has distinctly exhibited to consumers the flaws of the traditional system. Second, the evolution of new technologies that boosted mobility, easy flow of information, speeding up the service delivery and lowering the costs. The way banks engage the customer today has gained fresh prominence, with a movement from branch banking to digital systems, benefitting from customer insights. Aiming to enhance consumer engagement and gain competitive advantage, debates continue about the best strategies banks adopt to engage with customers that has resulted in adding capabilities and complex technology on top of systems and processes to meet dynamic customers’ expectations to gain real time personalization. Much debated question is whether organizations with traditional framework are able to come up to such expectations becoming capable of disrupting industry by prompt digital delivery using advanced algorithms and digital platforms to successfully provide unrestricted access to information bits. Promising superior experience to users, however, if industry experts hit the bulls-eye and tend to offer more competitive prices, enhanced operational controls may entitle lesser risk and probability of higher revenues. Such developments bring along advantages and disadvantages at the same time. Whereas advantage lies in reduced transaction processing times, service excellence and global integration the disadvantages lie in the fact that not many users are keen to shift to the fintech modes as far as their financial transactions are concerned since they are apprehensive of the risks associated with such adoption, witnessing this paradigm shift in the pace at which industry is developing focussing on much saturated red ocean of retail banking and gradually making a shift towards payment systems, which seems to be an untapped blue ocean of opportunities. The underpinning factors that govern the financial industry are KYC / AML and CFT together forming the basis for regulatory controls. Ethical transparent business knit together with service excellence, minimal risk, a strong regulatory framework in the competitive industry has given the incumbent banks an opportunity to partner, collaborate and codevelop with technology and consulting firms for collaborative innovation. Technologies like artificial intelligence, blockchain are capable of providing effective product suite to clients resulting in scalability of business.. On the other hand, robots replacing front end customer interface causes threat of redundancy to human capital and increase the training costs. As a result, many times an informal approach to collaboration tends to delay the outcome since the senior management looks at digitization in transaction banking as a profitable step and the middle management is hesitant of human redundancy, training etc which might cause delay. Taken together, a probable explanation advocates that common goals of digitization in financial institutions are regulatory control, risk mitigation, increase in revenue and to meet dynamic customer expectations by co-developing with external partners to gain competitive advantage. This adoption may further lead to higher cohesiveness in departments, improved value chain and reduced turnaround time with higher resolution quality. The digital transformation is capable of reducing the operational costs and overheads leading to increased profits, improved efficiency, better regulatory controls with less risks and collaborative opportunities for partners taking benefit of its tech talent to reach desired results. References: Anikina, I.D., Gukova, V.A., Golodova, A.A. and Chekalkina, A.A. 2016. Methodological Aspects of Prioritization of Financial Tools for Stimulation of Innovative Activities. European Research Studies Journal, 19(2), 100-112. Boston Consulting Group 2018, Three Keys to successful digitization in Transaction Banking, Boston Consulting Group, pp1-2 Botta, et.al.,2016. Technology innovations driving change in transaction banking. [Online] Accessed on March 11, 2018 Available at:https://www.mckinsey.com/industries/financial-services/our-insights/technology-innovations-driving-change-in-transaction-banking Capegemini & LinkedIn, 2018, World Fintech Report, p. 9-10 Hammond, Alex, August 2017, How banks are getting the digitization of core banking wrong, pp 1-8, [Online] [Accessed on April 02, 2018] Available at:https://www.bobsguide.com/guide/news/2017/Aug/23/how-banks-are-getting-the-digitisation-of-core-banking-wrong/ Johnson. et. al., 2017, Exploring Strategy chapter 3 Industry and sector analysis, pp. 62-91 Markovitch, Shahar & Wilcott, Paul, May 2014, Accelerating the digitization of business processes, Digital McKinsey, McKinsey & Company, pp 1-5 Mehrotra, Mohit, 2014, Digital Transaction Banking: Opportunities & Challenges, Deloitte Consulting Pte Ltd., pp 1-22 Olanrewaju, Tunde, July 2014, The rise of the digital bank, Digital McKinsey, McKinsey & Company, pp 1-5 Puschmann, Thomas, 2 February 2017, Fintech, Bus Inf Syst Eng 59(1): 69-76, Springer Fachmedien Wiesbaden 2017 Saksonova, Svetlana, Kuzmina-Merlino, Irina, 2017, Fintech as Financial Innovation – The Possibilities and Problems of Implementation, European Research Studies Journal, Volume XX, Issue 3A, pp 1-14

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Artificial Intelligence With Mobile Banking Is Reshaping the Future of Banking and Finance

Article | August 17, 2021

This is the flourishing time for financial services. The concept of interrelated technologies is developing and is brought forward due to the disruption in the industry such as that of cloud computing, data science, biometrics, and blockchain. However, most of the change has been brought about in the banking sector due to the introduction of artificial intelligence (AI). It is being expected that AI will bring a significant change in the industry in the near future and all of it will be significant.

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Optimizing Risk Management With Machine Learning In Banking

Article | August 17, 2021

The enormous amounts of data accessible to banks and their high demand for forecasting make the financial industry a perfect area for machine learning (ML) to shine. In this article, we explore the current applications of machine learning in banking when it comes to risk management, define its challenges and provide a future outlook. Credit Risk Management For the past few decades, banks have mostly used logistic and probit regression models for credit risk assessments and internal risk management. However, all conventional models inherit the same flaw — they predict outputs based only on linear relationships between input variables. This limitation was exposed in the catastrophic 2008 housing market crash. Although the crisis’s negative consequences have been multiplied by uncontrolled sales of credit default swaps and other complex financial instruments, the fundamental reason for failure was in the inaccurate credit risk model. In the aftermath, with the intent to force financial institutions to provide more detailed reports, The Federal Reserve’s CCAR now requires banks to account for more than 2,000 economic attributes. Consequentially, this also led to other regulating authorities introducing new standards that improve supervisory data quality and reporting. At the same time, with the proliferation of banking apps, social media, and digital communication overall, financial institutions now collect lavish amounts of unstructured data. If gathered and processed correctly, these new datasets can help gauge critical insights for a wide range of banking operations. This is where machine learning comes into play. More advanced non-linear approaches to credit risk modeling including neural networks enable banks to make predictions with a previously unseen level of accuracy and granularity. Challenges The utter superiority of machine learning over traditional credit risk modeling approaches comes at the cost of the prevailing ‘black box’ problem. While we can decide to trust ML algorithms based on statistical evidence of their feasibility, current regulatory constraints won’t allow it to happen. However, machine learning can still be used to a great extent while being regulation-compliant. Even simple linear machine learning approaches still yield more accurate results than conventional ones. Many banks also use unsupervised machine learning methods to explore data, while using traditional classification and regression models to make predictions. Fraud Management and Surveillance Nowadays, the majority of banks’ fraud detection systems use rule-based approaches. This causes banks to deal with a significant number of false positives, forcing them to spend inordinate amounts of resources to distinguish meaningless behavioral deviations from real threats. The ability of machine learning to capture subtle trends and uncover non-linear relationships allows banks to get a complete picture of a client’s activity and significantly lower the probability of false positives. For example, by integrating ML into its fraud detection model, Danske Banks managed to reduce false positives by 60%. Challenges Similar to many other AI-based solutions in the financial space, the biggest adoption hurdles concern regulations and the unexplainability of AI systems. For example, depending on the jurisdiction, banks are often unable to provide developers with sensitive information related to past breaches. Next, the outputs of unsupervised monitoring systems sometimes can’t be explained, which makes them non-compliant. However, financial institutions have found a way to at least partly leverage the power of ML for fraud management. A fraud prevention system’s alerts will still be triggered by rule-based models, but the integration of an ML algorithm on top of them can allow adjusting surveillance methods to a person’s behavior fluctuations. Such ML models are typically less complex and explainable, which makes them applicable in a regulatory context.

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Financial Asset Management Systems, Inc. (FAMS)

Financial Asset Management Systems, Inc. (FAMS) is an Account Recovery Solutions provider that has developed an industry-leading, multi-tiered approach to loss prevention, account rehabilitation, and revenue recovery.

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