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Open Banking Technology聽Architecture Whitepaper

Financial Services | API Management | Open Banking

Open Banking Technology聽Architecture Whitepaper

February 13, 2024

Financial聽Data聽Portability Best Practices

We developed this resource to help technical teams adopt an Open Banking approach by explaining a high-level solution architecture that is organization agnostic.

Open Banking Implementation DemystifiedWe also explore technology best practices to make your Open Banking adoption journey a success.

The Value of Speed: C.D. Howe Institute policy research quantifies the benefits of a modern payment ecosystem in Canada

Financial Services | Insights | Payments

The Value of Speed: C.D. Howe Institute policy research quantifies the benefits of a modern payment ecosystem in Canada

January 10, 2024
payment cards

With Consumer Directed Banking back on track for 2025, there is perhaps cause for optimism that a long awaited evolution in Canada’s payments ecosystem could become a reality over the next few years.

In addition to providing a comprehensive summary of the benefits of a faster, more open and competitive payments ecosystem, the opportunity cost of not evolving our payments landscape to include real-time payments (RTR) has been well documented in a recent research paper by the C.D. Howe Institute.聽 The paper also discusses specific actions that government and regulators can take to create a more innovative, competitive and fair payments ecosystem in Canada.

The full paper is available for download directly from the C.D. Howe Institutes website but it is worth highlighting several key themes/stats from the paper.聽 Many of these themes are already reflected in our payment strategies and implementation approaches for our clients.

Our team is optimistic about a better future for Canadian payments and will be following developments in this space closely.聽 As always, we look forward to working with ecosystem participants to learn more about their perspectives and how they view the considerable opportunity to win in our payments economy.

$3.25 Billion

Anticipated Impact to CDN economy over 5 years.

20.5 Billion

Total Payment Transactions in 2022

$2.23 Billion

Projected 5 year savings with RTR

RTR By The Numbers

The C.D. Howe Institute team provided some excellent analysis on the expected economic impact to Canada鈥檚 economy with a more competitive payments landscape enabled by RTR adoption.

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Open Banking Technology聽Architecture Whitepaper

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February 13, 2024
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2023 Fall Economic Update on聽Consumer-Driven Banking 馃コ

Financial Services | Digital Banking | Fintech | Open Banking

2023 Fall Economic Update on聽Consumer-Driven Banking 馃コ

November 29, 2023

When we attended the CLA Lenders Summit a few weeks ago, it seemed like everyone was ready to sign the death certificate for Open Banking in Canada. Very little, if any, material progress had been communicated on behalf of the government to create a regulatory framework or policy mandate to make good on the promise or timelines defined by an Advisory Committee on Open Banking Report which became the mandate of Abraham Tachjian, who was appointed as Canada’s Open Banking lead, by the Liberal government in 2022.

Fast-forward to Wednesday November 21st, 2023 when Finance Minister Chrystia Freeland provided her 2023 Fall Economic Update, our team, as well as many of our clients and industry collaborators were taken by surprise when the Liberals laid out a framework and timeline for Canada to implement a Consumer-Driven Banking approach.

Aside from a snazzy rebrand of the open banking moniker, the policy statement on Consumer-Driven Banking outlined the goal of “adopting legislation and fully implementing the necessary governance framework by 2025”. It would seem from the update, part of the government’s motivation for moving ahead with consumer-driven banking is a focus on the affordability crises facing many Canadians. The Consumer-Driven Banking policy statement was listed under the header of 鈥淢aking Life More Affordable鈥 in the economic update.聽 The update for Canada now seems to be following a similar path to how policy is being shaped and implemented in the US.

Our Take:

As long-term believers in the benefits of Open Banking, we are thrilled to learn of the renewed commitment to making consumer-directed banking a reality in Canada.聽 While there is still a lot of work to be done as well as the additional hurdle of new聽 legislation being tabled during an election cycle, we are committed to the potential聽 benefits of Consumer Directed Banking for banking customers and the significant opportunity for innovation and collaboration in the Canadian financial services sector.

You can read the full Fall Economic Statement here but if you are short on time, ChatGPT did a pretty good job of summarizing the statement in bullets, below.

 

Introduction to Consumer-Driven Banking:

    • Allows secure transfer of financial data via API.
    • Aims to replace unsecure screen-scraping.
    • Enables access to data-driven financial services.

Benefits of Consumer-driven Banking:

    • Consumers gain secure access to innovative financial tools.
    • Greater control over financial data for improved outcomes.
    • Small businesses experience reduced administrative burden.

Policy Objectives:

    • Safety and soundness of the financial sector.
    • Consumer financial well-being and protection.
    • Economic growth and international competitiveness.

Core Framework Elements:

    • Governance: Oversight and management of the system.
    • Scope: Types of data, participants, and expansion pace.
    • Accreditation: Requirements for participating in data sharing.
    • Common Rules: Privacy, security, and liability guidelines.
    • Technical Standards: Establishment and oversight of data flow.

Course of Action (Legislation in 2024):

    • Phased approach to scope, oversight, and screen-scraping elimination.
    • Key elements codified in legislation.
    • Mandate to a government-led entity for supervision and enforcement.

Governance (Effective Oversight):

    • Government-led entity to supervise and enforce the framework.
    • Model for provincial entities to “opt-in” to governance.
    • Strong governance framework to ensure compliance.

Scope (Phased Implementation):

    • Initial phase includes federally-regulated financial institutions.
    • Opt-in option for credit unions and third parties.
    • Reciprocal access for all entities to promote data portability.

Accreditation (Trusted Data Sharing):

    • Formal framework for entities collecting consumer data.
    • Regular reporting for accreditation maintenance.
    • Exemption for federally-regulated banks and credit unions.

Common Rules (Transparent Foundation):

    • Privacy, security, and liability obligations.
    • Compliance as a condition for data access.
    • Complements existing legislation.

Privacy (Protection Measures):

    • Participants must comply with legislative frameworks.
    • Specific privacy rules for financial data sharing.
    • Consent dashboards for real-time consumer control.

Liability (Clear Structure):

    • Statutory contractual relationship between participants.
    • Liability moves with the data and rests with the party at fault.
    • Internal policies and procedures for complaint handling.

Security (Protecting Consumer Data):

    • Clear security requirements for accredited entities.
    • Oversight of security standards in legislation.
    • Ongoing reporting obligations for data protection.

Single Technical Standard:

    • Mandate for a single technical standard.
    • Legislation to outline principles and oversight.

Next Steps for consumer-driven banking:

    • Department of Finance aims to implement the framework by 2025.
    • Ongoing engagement with industry stakeholders.
    • Legislative framework development and phased implementation.

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The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

Financial Services | AI | Enterprise Tech | Generative AI

The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

November 27, 2023
Image of a garden

At this point, enterprise generative AI applications for 聽software development,聽marketing,聽customer/employee service聽, and聽product design are proliferating as the standout use-cases early adopters are focusing on. From our position working with clients to develop and enable enterprise data + AI solutions, we’ve gained perspective on several of the foundational characteristics enterprises will need to develop to realize on the promise of this 3rd wave of the human-computer revolution.

Even though it is still early days we are very enthusiastic about the prospects of LLMs and powerful ML capabilities to unlock vast economic value and growth for progressive enterprises. Some are estimating an impact of $1 trillion, or 4% of GDP, in the U.S. alone and the generative AI market is expected to see rapid growth, rising from 11.3 billion USD in 2023 to 51.8 billion by 2028.

But First, a Disclaimer

Blanc Labs focuses on the financial services and healthcare industries. We see both sectors as having massive potential in terms of the benefits offered through the application of these transformative technologies.聽 Both healthcare and financial services are hugely complex, highly regulated, and depending on who you speak to, resistant to change. We believe the stage is set for a broader divergence of winners and losers: organizations that build capability and competency in adopting new tools and leverage the power of their enterprise data will stand to reap the benefits of their investments.

So with all the hype surrounding generative AI and the rapid advancement of a field that feels to moving forward at the speed of light, it is important to understand why some organizations will be better suited to create value in this field than others, and what organizations can do to ensure success.

鉀旓笍 Cloud Infrastructure Only 鉀旓笍

AWS Enterprise Strategist and Evangelist, Phil Lebrun is a pretty smart guy. In one of his posts about the topic, his flagship advice is “Don’t try this without the cloud.” You want your teams focused on problem-solving and innovation, not on managing the underlying complexity and cost of enabling infrastructure and licenses. The cloud is the enabler for adopting AI, making available cost-effective data lakes, sustainably provisioned GPUs and compute, high-speed networking, and consumption-based costing.

Enterprise Platforms are a Lynchpin

If we consider that having completed a cloud migration or cloud native approach is table stakes for AI聽 enablement within an organization, the next big question will be where your enterprise data resides. The organization’s technology infrastructure and enterprise platform ecosystem will in many ways determine what toolsets may be available to facilitate experimentation and development of AI enabled use cases.

If you are a CIO, CTO or CDO, start by taking stock of the platforms that house your enterprise data and evaluate the toolsets offered by these power technology-ecosystem juggernauts. Microsoft聽and AWS are emerging as clear leaders in terms of providing their customers powerful tools to apply ML and AI to their enterprise data, but Salesforce, SAP, Pega and OpenText – amongst many others will offer tooling to access, manage and apply AI to enterprise data.

It is impossible to predict who the big winners and losers of this space will be, partially because they are such massive and well entrenched players but keep an eye on this space over the next few years.

The 馃懣 is in the Data [Taxonomy]

Building an enterprise data model across a complicated operating environment like a bank is no small feat. Standardizing data definitions between different lines of business in a bank involves tackling the complexity of enterprise (+legacy) platforms used to store and handle data. This complexity extends to accommodating the diverse needs of various stakeholder groups with distinct preferences for accessing, manipulating, and analyzing data.

In many ways, risk is a unifying theme and responsibility within regulated industries like the banking sector. We see conformance to risk measurement and regulatory reporting requirements as a lightning rod for action amongst FI’s to get their “data house” in order but proactive approaches amongst banking and technology executives are few and far between.

In a recent interview with Tech Exec magazine, VP of Data and Adaptive Intelligence at Munich Re Canada Branch (Life), Lovell Hodge encapsulated the shift in how progressive organizations are thinking about their data:

“Over the years, there has been a realization that data has an inherent information value to the organization. As the years go by, we鈥檙e rapidly producing more data at an increasingly and somewhat alarming rate. So, what a lot of folks in the industry have realized is that there is not a huge space between theoretical concepts around information from data, and how they can use that information to simplify internal processes and also benefit clients.
So, you鈥檙e seeing that interdependence starting to mature. And because of that, many organizations are looking at their data not as just something they have to store, but as an asset they can leverage. And once you start thinking in that sort of way, then you start to formulate methods by which you can gain value from data, and it becomes an important part of your corporate strategy.”

App Design and Development Now Occurs ‘In the Data’

Historically, software design and development occurred in a sandbox that was devoid of enterprise data. In traditional dev environments we piped in dummy data to test parameters and during QA tested connectivity and security. But with the explosion of sophisticated ML and AI tools, application development will very much occur at the nexus of querying, manipulating, analyzing and visualizing proprietary data sets.

There is a tremendous amount of opportunity for these new tools to democratize data for business users through the use of conversational/natural language query tools to conduct analysis and derive insights from enterprise data sets.

Another big change will be the blurring of the lines between the role of developers and data scientists and in many cases, these resources will be working directly alongside each other.聽 Technical resources will require access to live data sets to facilitate model training and to leverage powerful data analytics toolsets. This is going to require a significant shift (from reactive to proactive) in terms of access management and data security.

Firms that work with external partners will require them to adhere to an extremely high standard of data security protocols i.e., ISO and SOC 2 compliance certification with regular audits.

Specific sector, domain, and knowledge of business context has always counted for a lot but increasingly developers won’t be able to rely on detailed functional, technical and design requirements documentation to define the specs of what they are developing.聽 It will require a much more experimental, test and learn approach.聽 Toolsets are only going to get more user friendly to work with but developing high impact apps leveraging advanced AI/ML techniques will require a detailed and nuanced understanding of user needs, workflow, data inputs, command prompts, prompt modelling strategies, and a continuous improvement approach to get to a place of lasting and tangible impact for businesses and teams.

In Summary:

There is much still to be learned through this large-scale adoption of a suite of technologies that are evolving at such a rapid pace. This is by no means a complete list of the things business teams and leaders need to consider as they look to capture competitive advantage from these powerful new tools. At Blanc Labs, we are excited about the future and look forward to working with companies to develop their capabilities to create business value by harnessing the tremendous potential of their enterprise data.

About the Author

David Offierski
VP Partnerships & Marketing

With over 15 years in technology consulting services, I am thrilled at how next-gen technologies will unlock a new wave of innovation and productivity for enterprises.

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