Account Opening Transformation

A major area of focus for carrier-owned broker-dealers has been improving and modernizing new account-opening processes. Many firms are exploring digital account-opening solutions to replace antiquated, paper-based systems. Goals include reducing operational inefficiencies, minimizing errors/delays, and providing a seamless user experience. As part of an effort to solve these problems, vendors are providing end-to-end digital workflows. Key challenges center around integrating external platforms and ensuring uniform processes when working with third-party asset managers.

Harnessing AI and Automation

We discussed firms’ efforts to adopt generative AI and automation tools to boost customer service and staff productivity. Use cases include leveraging chatbots for basic inquiries, applying natural language processing for document analysis, and using machine learning for predictive analytics.

However, prudent concerns exist around responsible AI adoption given the highly sensitive data within our industry. There is wariness about potential faulty recommendations without sufficient human validation. As such, measured adoption in the near term is most likely vs. wholesale replacement of staff.

Guidance around appropriate AI use for activities such as marketing and research has been provided to advisors by most firms, but ongoing supervision is difficult. Firms are analyzing activities to clamp down on unauthorized usage. The goal is for AI to enhance individual productivity rather than fully supplant roles, with workers shifting to higher-value analysis and judgment-oriented tasks. Striking the right balance between automation and human involvement is imperative. Ongoing governance and oversight will remain crucial to ensure consumer interests are protected as firms integrate AI.

Mitigating Vendor Contract Pressures

Vendor contract negotiations was another major topic discussed. Many vendors are imposing inflationary increases and trying to limit negotiation leverage. This is especially true for dominant solution providers, such as Broadridge, after recent acquisitions. Firms realize consolidating all use cases can help maximize negotiation power. But it remains an uphill battle, and we expect further vendor consolidation ahead. Creative partnerships may be needed to disrupt incumbent vendor dominance.

As always, it was insightful to hear how our peers are leveraging technology to bolster operations while balancing risk, efficiency, and user experiences. Our Broker-Dealer Research Group continually explores these and other critical issues. If you would like to join our discussions on the latest broker-dealer technology trends in the coming year, please visit our website or reach out to me at mwein@datos-insights.com.

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Insurers Have The Choice of Several Paths When It Comes to LLMs

Insurers have a range of approaches to choose from when they engage with generative AI:

• Insurers who want to develop and control their LLM may choose to build their own. This path will likely be rare due to the prohibitive cost of building one.
• Other insurers will choose to do some fine-tuning on a preexisting LLM, which involves refining the model with partners to create an offshoot model focused on a specific area.
• Insurers may also leverage language model capabilities from a proven insurance solution provider or a new insuretech player in the space.
• Lastly, insurers may choose not to pursue the use of LLMs at all, perhaps building a small language model instead and using open-source tools for specific limited purposes.

Datos Insights expects fewer than 1% of insurers to build LLMs and around 5% of insurers to fine-tune an LLM. Most insurers will leverage a solution provider’s AI capabilities or partner with insuretech companies.

Many Insurers Are Not Engaging With This New Technology

Despite widespread interest in AI and related technologies, many insurers are not engaging with newer technology, instead sticking to uses that pre-date 2023, such as expert systems, machine learning, and machine visioning. However, insurers who are not investing any effort or mindshare into these topics do risk placing themselves at a competitive disadvantage. Few insurers are likely to build their own data science foundations, but understanding the concepts and options around AI will be necessary for insurers to leverage, invest in, or effectively work with external entities.

The Decision of Whether or Not to Leverage LLMs Should Be Strategic

No matter which path insurers choose when it comes to LLMs, the decision should be strategic, with specific use cases outlined where the use of the technology will add value. There are several different strategic parameters they will need to consider, including budget, insurer data science expertise, insurance domain knowledge, and competitive advantage.

For more information about the value and future of AI language models in the global insurance industry, read our full report on the subject, The Value and Future of AI Language Models in the Global Insurance Industry, October 2023, sponsored by DXC.

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However, these projects may require more financial investment and organizational attention than originally anticipated, and they are at high risk of running over budget and schedule while failing to deliver promised benefits. As insurers move from initial AI proofs of concept, they will need to consider a number of factors around AI project readiness:

• Business readiness: The carrier must ensure that the stakeholder business units are ready for the project and have a clear vision of what the project should achieve, as well as clear guiding principles and responsibilities for decision-making.

• IT and technical readiness: Solutions architects need to map out the required architecture, including open-source architecture, create an inventory of required interfaces, and establish principles and processes to govern technology and model decisions around training, drift, and hallucination. These architects may conclude that, in some cases, small language models that have more restrictions on the type of data they can utilize but avoid hallucinations may be a better fit architecturally for the problems to be solved.

• Data readiness: Garbage in, garbage out. Insurers must ensure that the data is well organized, easily accessible, and cataloged with the appropriate business rules to ensure responsible AI outcomes around personally identifiable information and personal health information. If this isn’t done, the results of the model could be incorrect, misleading, or expose the firm to regulatory fines and reputational damage.

• Program readiness: Carriers need to staff their AI programs with key roles such as pod leader, prompt engineer, model mechanic, data engineer, and data scientist. They also need to work with vendor and systems integrator partners to define the scope of the project and plan execution. But where to get these folks? This is a challenge many carriers will face. The solution inevitably will come from unique recruiting programs on college campuses, summer internships, and re-training of staff who have an interest or are in similar adjacent roles today, where their skills can be augmented.

• AI maturity: Insurers need to understand how close the organization is to becoming an “AI Master” from a people, process, and technology perspective. The insurer needs to determine if it is prepared at an enterprise level to make informed business decisions utilizing AI or would rather use AI in niche applications, e.g., customer service piloting, application submission process optimization, enhanced claims adjudication, computer-assisted underwriting). It will initially be difficult for the organization to assess its maturity, and this maturity at midsize to large insurers may be uneven across business lines.

Addressing the above challenges will not be easy. Datos Insights recommends a central point of coordination for AI in general, as opposed to each department or division “doing their own thing.” Proofs of concept will help determine in which areas organizations should perform AI projects.

The enterprise needs to start looking at the AI big picture (i.e., the items listed above). Once a full AI project or projects are chosen, the means to achieve success should already be in place. The alternative to this thoughtful approach is a more hype-driven, opportunistic approach to AI, which will inevitably lead to higher costs and delayed deliveries, discrediting the technology and the IT organization that drove it.

For more on generative AI, see Datos Insights’ report Top Five Questions Insurer CIOs Have About Generative AI, September 2023. Please also feel free to reach out to me for a conversation around AI readiness in your own organization.

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But quantum computing is one area of emerging technology they may not be able to afford to neglect. So, what do banking IT leaders need to know about quantum? And how can they prepare for this emerging technology without breaking the bank?

Current Stage of Quantum Development

Quantum technology is not yet in deployment at most financial institutions, but several pilots and proofs of concept have been tested with promising results, especially by larger banks. Bank of Canada and Crédit Agricole have completed proofs of concept involving cryptocurrency adoption, valuation of financial products, and credit risk assessment.

Aite-Novarica Group estimates that specific use cases combining quantum technology with artificial intelligence (AI) will have practical applications for banks by the mid-2020s. Binary core systems and applications could interoperate with third-party quantum solutions via microservices and APIs by 2025.

While quantum computers hold the potential for great gains in efficiency, the risks they pose to present-day security methods have also begun to loom over the financial services industry. Quantum computers have the potential to decrypt traditional methods of encryption, and furthermore, quantum computers and simulators are now accessible to the public via the cloud, meaning bad actors also have access to the new technology.

Even before decryption is possible, banks could be targeted in “harvest now, decrypt later” attacks where the encrypted data is stolen and kept by the bad actor in anticipation of quantum technology that will become available in the future.

What Should CIOs Do Now?

So, what can CIOs do today to prepare for the quantum revolution? One key step is moving core systems to the cloud, a process which banks have largely already begun. Once banks are on the cloud, they will be able to take advantage of new technology as soon as it becomes available via regular vendor updates, as opposed to the lengthy replacement process required by legacy solutions.

The combination of AI and quantum, known as quantum artificial intelligence (QAI), yields particularly pertinent applications for banks such as risk assessment and modeling. Banks can prepare for these use cases by continuing with any roadmap activities involving AI. Once QAI becomes available, it will be much easier for banks to implement if they are already using traditional AI and/or machine learning solutions.

Technology leadership should also ensure their staff are staying up to date on quantum news as a preliminary step to upskilling. There is already a shortage of skilled IT staff, and educational institutions have only just begun offering industry-preparation quantum degree programs such as master’s and bachelor’s degrees. Aite-Novarica Group predicts a severe shortage of quantum talent within the next decade as demand grows to far exceed the limited supply.

Once a quantum-aware culture has taken root, executives can consider offering existing staff mutually beneficial growth and development opportunities. Partnerships with local educational institutions can also be a great resource for obtaining newly qualified talent with in-demand skill sets.

In 2023, quantum computing (and its associated risks and benefits) is no longer the stuff of science fiction. The good news is that even small-to-midsized institutions can take practical steps to prepare for the quantum revolution.

These include continuing on the cloud journey, exploring AI solutions, and educating existing staff about quantum computing. For more on this topic, read my latest checklist report, designed specifically for community banks and credit unions: CIO/CTO Checklist for Community Banks and Credit Unions: Implications of Quantum on Technology Roadmaps. IT leaders are also welcome to join our upcoming Research Council meeting, which will take place on May 11, 2023 and will focus on quantum technology this quarter. Contact me at mwein@datos-insights.com to register.

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In our upcoming webinar, Driving Data Maturity Through Strategic Planning, I will be joined by Aite-Novarica’s Head of Banking & Payments Christine Barry and Republic Bank of Chicago CIO Madhu Reddy for a panel discussion on how small to midsized financial institutions can institute strategic planning processes around data and advance their data maturity level.

Creating a holistic data strategy that addresses all aspects of the data ecosystem enables banks to truly partner with their clients, deepening relationships and leveraging opportunities for cross-sell throughout the business. Sharing data across the bank reduces friction in all manner of processes, including onboarding and online account opening.

Banks that take the right steps toward achieving a high level of data maturity will have an edge over the competition—including other banks and fintech firms—when it comes to meeting ever-heightening customer expectations. They will be able to identify and resolve problems more quickly, gaining both tactical and strategic advantage in the long term.

However, it can be challenging for IT leaders to identify where to start and how to proceed on the path to data mastery. Aite-Novarica research on data identifies several best practices for IT organizations at community banks and credit unions, including the following:

1. Enforce data governance using policies, procedures, and automated tools. Tools are very useful for saving time, but on their own they will not be enough to ensure success. Enterprise-wide policies and procedures such as a data dictionary or thesaurus save time and reduce confusion across the organization.
2. Consider data an asset whose value can be increased by combining and transforming. Banks have access to more data than ever before, and many institutions choose to augment their internal stores with third-party data. Once connectivity has been established throughout the data ecosystem, the potential for insights multiplies.
3. Incorporate data volume, variety, velocity, validity, and veracity into the data roadmap and architecture blueprint. Creating enterprise artifacts, such as a data roadmap and an architecture blueprint, is key to ensuring that data maturity initiatives stay on track over time. These artifacts should address the realities of the big data age—volume, variety, and velocity—while also ensuring validity and veracity. If the data isn’t reliable, it can undermine the entire strategy.

Given the regulatory and market risks of ignoring data needs, and the numerous benefits that banks can obtain through maturity, it is no wonder that data mastery is a crucial priority for small to midsized FIs in 2023. For more information, ideas, and best practices, click here to register for our upcoming event Driving Data Strategic Planning and Maturity.

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That said, choosing the right tool is only one piece of the data insights puzzle for CIOs and CTOs in 2023. My latest report on data visualization tools provides both a market overview and a checklist for IT leaders on optimizing their overall data ecosystem to make the most of their visualizations.

Open-Source Tools

Open-source data visualization tools are tempting, as many of them are free to use. Well-known open-source tools include Google Charts and D3.js. Options like this can seem like a great way for smaller community banks and credit unions to save money.

But tools like these can open the door to more potential pitfalls. Financial services institutions must adhere to steeper privacy regulations than other industries. There is no vendor or central governing body to liaise with about security risks. As the source code is available for anyone to edit, it can be a target for hackers.

Not only that, but the source code sometimes includes snippets of code from vended solutions. That means that, depending on the open-source licensing, users may owe money to a vendor or individual even though they think they are using a free, open-source tool. While financial institutions should always ensure that a process is in place for legal and operational management prior to implementing any data tool, this is especially important when it comes to open-source software.

The Existing Data Ecosystem

The ideal data environment includes a data staging area, an operational data store, a data warehouse, an optional data lake (for big data and unstructured data), and data marts. Among these key components, data analytics and visualization tools are usually directly connected to the data marts.

Not all financial institutions have achieved this ideal environment in 2023. Individual financial institutions are at different stages of the data journey; furthermore, no two institutions’ data ecosystems are exactly alike. Data visualizations frequently offer a range of connectivity options and APIs, which community banks and credit unions must compare not only to their existing architecture, but also to the future IT roadmap to facilitate long-term success.

Types of Visualization Tools

In today’s marketplace, there are a variety of visualization tools with capabilities that are leverageable for problem-solving. Some of these, such as code tools, require highly skilled resources whereas others have a business-friendly interface. Different types of tools offer different levels of customization, types of visualizations, and interactive components.

Visual reporting and business intelligence tools can often connect to multiple data sources for processing visualizations, dashboards, and reports. Network graphs are useful comparison tools and are frequently used in the financial services industry for fraud/AML monitoring. Common banking use cases for data visualization tools also include forecasting and risk management, among others. Community banks and credit unions wishing to achieve a data-driven model can derive significant value from combining different tools depending on the issues at hand.

Click here to view the report webpage, and email me at mwein@datos-insights.com to find out how you can get access if you aren’t already a Community Banking client.

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Institutional Best Practices Impact the Effectiveness of Data Visualization Tools

Small financial institutions must ensure that they have infrastructure and governance practices in place to reap the benefits of the tools they plan to install. Even if you’ve adequately assessed your requirements, don’t neglect to lay the necessary groundwork. Choosing a tool that meets business and IT needs is important, but the latest technology won’t make up for a lack of institutional best practices. In our premeeting survey, one member shared a cautionary tale: Their tools were fit for purpose, yet issues with implementation and connectivity to data stores meant that they were not producing the necessary insights.

Data Governance and Zero-Trust Is a Must in the Age of Big Data

There is no shortage of ancillary factors for CIOs and CTOs to consider. Even more important than the tools themselves are the data sources those tools are pointed to. Council members were in consensus that cleaning and organizing data is necessary to obtain high-quality insights, but doing so effectively is easier said than done in the age of big data. One Council member summarized the situation as follows: “Ten years ago, we struggled because there was too little data. Now, the challenge is that we are inundated with too much data.”

Establishing an enterprise data governance model can help tremendously with reducing the time, resources, and headaches associated with data management. Any organization using data visualization tools should also include a zero-trust policy in its governance initiatives. Some institutions have learned the hard way about the ever-present threat of ransomware attacks. In addition to other undesirable effects of security breaches, maintaining regulatory compliance is a key consideration for community financial institutions. Privacy and regulatory policies that may apply must be considered before stored data is used in reporting and analytics.

How You Can Get Involved

The agenda of each quarterly meeting is driven by Research Council members’ interests and priorities. On May 11, we’ll meet for our second quarterly meeting of 2023. Click here to learn more about our Q2 meeting.

We are always happy to meet with prospective members, including senior IT executives at community banks and credit unions. Have further questions about or thoughts on the Research Council or data visualization tools? Email me at mwein@datos-insights.com, and I’ll reach out to schedule an introductory conversation about the Research Council and the Community Banking practice.

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Consider the cloud type: public, private, or hybrid

When it comes to the cloud, there are public, private, or hybrid options. Almost all banks use the cloud today, and many use multiple types of clouds. Public clouds are open to all companies that pay for access; infrastructures are shared among consumers. Private clouds are restricted to a specific company and exist behind a firewall, so third parties must be permissioned to access them.

Because so many solutions on the market today are cloud-hosted, IT executives should take the cloud type into account during the software selection process. While private clouds can be expensive to build, the limitations of public clouds can result in cost implications down the line. On the other hand, hybrid clouds built using containers serve to safeguard longer-term flexibility.

Evaluate strengths and weaknesses of software types

Several types of software are available on the market today, including cloud-based, hosted, and managed service solutions. When weighing their options, banks should consider the long-term costs and talent needs as well as the security and scalability capabilities, which also vary. Caution is necessary with open-source solutions, as they appear to be free but can be susceptible to hidden licensing fees if they incorporate code from a vended product. In-house services can cause difficulty down the line if the employees operating the software leave, taking their knowledge with them.

Mitigate solution provider dependency risks

Package solutions or Infrastructure-as-a-Service, (IaaS), Platform-as-a-Service (PaaS), or Software-as-a-Service (SaaS) all risk solution provider dependency. IaaS, PaaS, and SaaS limit visibility into solutions’ workings, so package solutions could mean vendors control the security measures that banks are ultimately responsible for. Banks should request results from audits and other third-party security assessments before committing to working with a vendor. If a package solution is chosen, it’s also important to enact risk mitigation procedures to cover areas including source code escrow and security controls prior to implementation.

While useful information can be obtained from a solution provider or other third party, these parties’ interests may not align with those of the bank itself. IT executives within the bank must be involved in making the decisions that will affect the bank for years to come.

The foundational transformation of core systems and surrounding technologies during the early 2020s affect all CIOs, CTOs, and heads of architecture whose financial institutions have adopted digital banking processes or components. The decision-making process around any software selection has become more complex and trickier to navigate. My latest report equips executives with the concepts, considerations, and market knowledge that are key to successful development. Click here to view the report webpage and email me at mwein@datos-insights.com to find out how you can get access.                                                                                                                                               

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ChatGPT is a natural language processing (NLP) chatbot utilizing OpenAI’s Generative Pre-Trained Transformer 3 (GPT-3) language model, created in May 2020. The model predicts the next token value of a response based on previous token values, leveraging a technique that utilizes unsupervised pre-training of the model with specific supervised fine tuning. It has been used to generate written paragraphs, website content, and papers that are difficult to distinguish from human-written prose. OpenAI published a paper, “Improving Language Understanding by Generative Pre-Training,” which describes the technique in further detail.

After ChatGPT came out, the educational establishment raised concerns that students could use it to “cheat” on term paper creation. Of course, other technology is already being developed to detect AI-generated prose. Notably, a new tool developed in January 2023 called GPTZero can read the prose and assign a score to indicate how likely it is that selected prose is bot-generated. Unsurprisingly, the student who developed the tool was interviewed on CNN on February 1, 2023.

Given the hype, I wanted to experiment with ChatGPT to see what it can do now and how it could evolve over the next few years. I logged in, created a test account, and cut and pasted my resume, the link to my LinkedIn profile, and the link to the Aite-Novarica Group website. From there, I asked a few simple questions and received answers; all of them were well-written, but many were inaccurate. Examples of questions I asked included, “Who is Mitchell Wein at Aite-Novarica Group?” “When did Mitchell Wein graduate from college?” and “Where did Mitchell Wein work at the turn of the century?” The bot told me it could not read the LinkedIn link. When I pasted in the section of my resume related to the question I asked, the bot responded with a well-written answer summarizing the information I had just given it. Interestingly, the bot could not remember facts from prior sections of the conversation. When I asked the bot about the forgotten facts I had provided it just a few minutes earlier, it gave me this response:

“I apologize if I am not retaining the information you are providing correctly. As an AI language model, I do not have a memory and can only respond based on the information provided in the current interaction. Please let me know how I can assist you further.”

The bot also told me this: “As an AI language model, I do not have memory in the traditional sense like humans do. I have been trained on a large corpus of text, and I can generate answers based on that training. When answering a question, I do not store information about previous questions and answers in this conversation or any other conversations I have had. Instead, I process each question independently based on the information provided in that specific question and my training.”

The bot is purely text-based and cannot interact with pictures or graphics. This reminded me of early DOS prompt interfaces from the 1980s.

So, ChatGPT is still in its very early days. There is much more to do to make it a fully functional, general-use AI platform. Additionally, the underlying computing environment is still limited—especially regarding concurrent processing—even though it is in the cloud; quantum will solve that by 2030. When I was using the tool, it was slow at times and crashed at times. Also, it is not apparent how the data streaming through the ChatGPT platform will be protected from a security perspective. Has zero trust been deployed for users of this platform? I don’t think so! However, ChatGPT has massive potential.

Generally, firms are investing a lot of money in AI automation. Angus Loten’s article “Uncertain Economy Spurs Growth in AI-Powered Office Automation,” published in the February 2, 2023, Wall Street Journal, suggests that the pressure to do more with less has led a third of global corporate technology chiefs surveyed—more than 2,000 respondents—to anticipate that AI-powered automation software, including decision support and process/task automation, will account for the largest share of new IT funding. Microsoft is planning to integrate ChatGPT into its software and platforms.

Community banks need to start thinking about how they can leverage these new technologies as they continue to mature their IT ecosystems. Conversational AI platforms could be powerful tools in a community bank’s technology arsenal. That is, if the conversational AI platform can leverage everything available on the internet and vet the information against the insights needed (similarly to a search engine); interact with customers, regulators, and executive management to determine what they need; and has the APIs to connect to third-party data sources, fintechs, and back-end data and processes from the core systems and incorporate operational process documents. Possible use cases include loan processing and origination, customer onboarding, payments, settlements, cash management, generating regulatory responses and reporting, and cross-selling new products.

Of course, to meet regulatory muster, it will have to leverage all of the technology best practices, including zero trust, to ensure that communications and data moving between customers and the AI platform are not compromised or stolen. Change management will be another area that will need to be tightly governed to ensure that AI-based learnings are not corrupted through human error regarding change control and testing.

It is still very early days for this technology. However, community banking CIOs and CTOs must keep track of how AI evolves. They must also assess the impact of quantum on AI as it becomes available and what the core system and office productivity vendors are doing to incorporate AI platforms directly into their offerings. It is clear that the digital interactions that AI powers will be vastly different in 2030 than in 2023.

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In 2023, many IT organizations will need to juggle replacement and enhancement projects, new platform implementations, and architecture/structural updates, often putting great pressure on their operations.

Aite-Novarica Group has identified the 10 key trends that will impact IT executives at FIs, including CIOs, CTOs, and heads of architecture, in 2023.

For example:

• Digitalization accelerates. As banks and FIs of all sizes continue the digital transformation journey, they must prioritize customer-centric initiatives that align with strategic objectives. While institutions are at varying stages of their digital journeys at present, this won’t last for long, as they typically name digital as a key priority for 2023. It’s critical that institutions achieve a seamless customer experience if they hope to compete in the 2020s and beyond.
• Reengineered processes in financial services demand data quality and variety. Forward-thinking FIs are utilizing a combination of internal and external data to facilitate processes such as analytics-driven product design, artificial intelligence, and portfolio management. As enriched data increasingly becomes the norm for FIs, safeguards are needed to maintain regulatory compliance.
• FIs’ business processes span multiple clouds and architectural ecosystems. As technology continues to develop, a diverse range of cloud products and services have become available over the past few years. A major driver of the industry shift to multi-cloud is the fact that different approaches are often necessary within the same institution: For example, business use cases requiring enhanced calculation speeds frequently occur in market risk analysis, whereas credit risk has been slower to migrate to cloud due to regulatory obstacles.
• Banking depends more and more on APIs from third-party services and data providers. Institutions relying on third-party services have frequently found themselves responsible for ensuring those services’ regulatory compliance. As regulations continue to increase, FIs will need to assure regulators of the consistency, security, resiliency, and robustness of these APIs.

Forward-looking institutions will set themselves up for success in 2023 and beyond by establishing strong foundations to support concurrent initiatives, including clear ownership structures for new technologies and enterprise IT strategies backed up by digital artifacts.

For a detailed analysis of all top 10 trends for 2023, read the full report Top 10 Trends From Financial Services CIO/CTO Advisory, 2023: The Future Has Arrived. You can also watch the recording of our February 14th webinar, where we explored each of these trends in detail. Click here to access the recording.

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Artificial Intelligence Has Taken Off in Financial Services

AI and ML are increasingly leveraged by FIs to reinvent internal and customer-facing processes, leading to efficiency gains and improved service outcomes. These advanced technologies are being deployed across a range of use cases including automated investment advice, customer service chatbots, and anti-money laundering analytics.

With growing demand for AI/ML-based use cases in delivering on the promise of business transformation, FI IT leaders should explicitly address information capture and transparency regarding the design and use of AI-based processes and models in achieving strategic IT objectives.

The main consideration for CIOs and CTOs is that AI/ML-based algorithms should be built to adhere to the guiding principles, like fair and non-discriminatory outcomes and transparent decisions, enabling required regulatory reporting.

IT Architecture Must Be Designed to Promote Transparency of AI-Driven Processes

As I write in my latest report, CIO/CTO Checklist: AI and ML: Explaining Algorithm Outcomes to Regulators, “The FI’s enterprise and solution architecture must be designed to support [AI] and [ML] algorithms in a way that provides regulators the information they need to confirm alignment with guiding principles for these technologies.”

This means that FIs must be able to explain the way that AI-driven outcomes are generated to regulators, customers, and potential customers. To build transparency into how AI/ML is leveraged, basic principles of AI use like fairness and lack of discrimination must be designed into solutions/enterprise architecture.

By doing so, FIs will mitigate the risks (e.g., lawsuits, reputational damage) of the typical “black box” phenomenon, where the decisions and recommendations from the algorithms are difficult if not impossible to understand based on the inputs given to the algorithms.

FI CIOs and CTOs should embrace partnering with business leaders to adopt practices that support explainability as part of a comprehensive design approach.

Best Practices for CIOs/CTOs in Leveraging and Explaining AI Algorithms

My team identified six best practices to assist FI CIOs and CTOs in designing and overseeing the use of AI/ML-driven models and processes to promote explainability and transparency in adherence with regulatory principles. These are summarized in the table below:

Table A: The Checklist

The checklist in this table, also outlined in my latest report, facilitates IT leaders’ efforts to design AI/ML-based models and processes that both fit within the enterprise’s IT strategy and enable transparency in providing regulators with required information.

Exclusively for members of the Financial Services CIO/CTO Advisory Practice, banking and FI IT leaders can use these practices as a framework to design AI algorithms and AI-driven financial processes that are explainable.

Could it help to explore how these best practices can facilitate your organization’s initiatives to develop AI-based processes that adhere to regulatory requirements? Contact me at mwein@datos-insights.com to start the conversation.

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Finding the Sweet Spot Between Unified Experience and Open Banking

In Aite-Novarica’s Q4 budgets and projects Research Council survey, unified experiences across the bank emerged as a top priority for participating institutions, sparking a discussion about how it needs to go hand in hand with open banking. Council members felt that it was important to define unified experience flexibly, including building partnerships with fintech startups and executing initiatives such as omni-/multi-channel that bring immediate benefits to clients.

As part of the discussion, one participant brought up the idea of always up, never down. “Always up” is important in today’s banking IT world due to both customers and regulators. CIOs should address any issues with consistency prior to open banking capabilities or API integrations: If you have outages or instability, or if you’re not available digitally when people need you to be, they lose confidence.

Furthermore, there’s significant regulatory oversight to ensure that there are plans in place to address gaps: It is mandatory to adhere to regulatory compliance requirements around operational resiliency.

Addressing 2023’s Key Challenges: Talent, IT Ops, and Cost-Effectiveness

In our most recent survey, Council members indicated that IT operations and talent were particular pain points for their organizations. Council members discussed how to achieve efficiency in their IT budgets and best utilize their resources. In my experience, there’s an optimal size for outsourcing–too much risks brittleness in the supply chain, yet too little indicates overspending.

While Council members range from outsourcing more than half of their IT staff to none at all, the happy place right now seems to be somewhere around 18%. I’d also recommend a mixture of offshoring and nearshoring for banks that are considering outsourcing but are wary of having external staff all in one region. Western Canada is one region that banks are hiring from to achieve a compromise with staff closer by in North America.

During a difficult time for the economy, some Research Council members suggested funding new projects by repurposing their IT spend. Older vendors and software can be downsized if they are no longer needed for their original purpose, and that spend may be repurposed into 2023 initiatives. Areas of interest for banks in 2023 include operational data stores, such as data warehouses and data lakes, and customer onboarding systems.

How You Can Get Involved

The agenda of each quarterly meeting is driven by Research Council members’ interests and priorities. On February 16, we’ll meet for our first quarterly meeting of 2023. Click here to learn more about our Q1 2023 meeting.

Budgeting season is the time of year for CIOs, CTOs, and heads of architecture to examine the bigger picture, including the priorities, challenges, and plans of the IT organization as a whole. If you need advice on creating strategic artifacts such as a blueprint, roadmap, or current state assessment, email me at mwein@datos-insights.com, and I’ll reach out to schedule an introductory conversation about the Research Council and our FS CIO/CTO Advisory Practice.

The post It’s That Time of Year: Banks’ IT Budgets, Investments, and Initiatives for 2023 appeared first on Datos Insights.

Financial institutions (FIs) are realizing economies of scale by running software, data analytics, and digital business processes on pre-built cloud services that provide greater computing resource and software applications availability, power, and security than hosted platforms.

At present, FIs are at widely different maturity levels with cloud adoption. CIOs and CTOs at all stages of the cloud journey should consider how to address the complexity inherent to cloud deployment—whether they have started their journey or not, industry-wide trends are pushing FIs toward cloud services in order to accelerate digital transformation initiatives.

Financial Institutions Cannot Avoid a Multi-Cloud Environment

While on-premises hosting systems and servers will be maintained for certain functions, such as trading and risk management, migration of applications to the cloud is an essential driver of business model digitalization.

Financial services applications (e.g., online banking apps) and digital business processes are increasingly built and run on, and application services (e.g., fintech partner APIs) are increasingly being consumed, on multiple cloud services like Salesforce, Amazon, Microsoft, and Google. In the future, FIs will be unable to avoid migrating to multi-cloud environments due to emerging open-banking regulations, the facilitation of connected-banking capabilities, innovation in core systems packages, and the scale economics of running digitalized financial services processes.

While hosted solutions will still be part of the technology mix, pre-built cloud services offer significant benefits to FIs modernizing their processes and digitalizing service delivery.

Benefits of Moving to the Cloud

As I write in my latest report, CIO/CTO Checklist: Managing the Architecture of Multi-Cloud Environments, “Many financial institutions can run their applications in a faster, more consistent, and more highly secure manner in the cloud.” Cloud-based applications require less resourcing to maintain and provide efficiency gains as organizations can allocate computing resources to match needs in running internal and client service processes (e.g., providing client account analytics).

IT leaders who once perceived the cloud as insecure or fit only for specialized functions now accept that cloud services constitute the modern technology infrastructure that enables digitalization of financial processes and customer experiences.

Migration of legacy technology infrastructure to cloud services poses a complex set of challenges, especially in deploying a multi-cloud environment. For example, technology architecture has to accommodate the specific delivery models of individual cloud services (i.e., IaaS, PaaS, or SaaS). CIOs and CTOs must leverage a structured framework to align their IT blueprints and roadmaps to address the architecture considerations critical to success in the context of the cloud.

Best Practices for CIOs and CTOs in Deploying a Multi-Cloud Environment

My team identified ten best practices to assist FI CIOs and CTOs as well as their teams in implementing cloud-based environments. They are summarized in the table below:

The checklist in this table, also outlined in my latest report, facilitates IT leaders’ efforts to create or adapt their architectures, IT roadmaps, and budgets to enable the migration to the cloud. Exclusively for members of the Financial Services CIO/CTO Advisory Practice, banking and FI IT leaders can use these practices as a framework to effectively structure the implementation of cloud services solutions alongside hosted systems within the enterprise technology ecosystem.

Curious about how these practices can facilitate your organization’s migration to a cloud infrastructure? Contact me at mwein@datos-insights.com to start the conversation.

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Digital Ownership

Embarking on a digital journey without clear ownership can lead organizations directly into trouble. In Aite-Novarica Group’s Q3 digital strategy survey, we found that most FIs do not have a Chief Digital Officer, sparking a discussion on the best structure for ownership of digital initiatives.

Research Council members agreed that it’s ideal for ownership of digital initiatives to reside within the business, as it’s the most natural way to engage business stakeholders. If ownership formally resides with IT, as is often the case in smaller institutions, the digital owner(s) may encounter challenges getting business users to embrace the new technology. Therefore, the organization must be intentional about promoting engagement within the business and cultivating a sense of partnership.

Challenges and Resolutions in Communicating Digital Initiatives

Council members shared techniques and anecdotes for promoting the collaborative culture that serves as the foundation for an effective digital strategy. One IT leader said that cultivating individual relationships with business leaders can sometimes yield positive results. Another shared that having a C-suite proponent of digital engagement on the business side is the ideal situation, discussing the extent to which this has smoothed the digital journey at their institution. While resource availability is always a challenge these days, a C-level business champion can mitigate competing demands and priorities.

Whether there is one enterprise business owner or several departmental owners is determined by diversity of product lines. While divided ownership can be effective in certain organizational structures, it can also generate a lack of cohesion if there is not enough communication between the various business areas. FIs can solve this problem using a set of enterprise digital standards. Even if the different owners for each area have diverse priorities, giving all of them the same digital approach makes a world of difference and ensures a consistent, cohesive experience for clients and end users.

Training and Education Are Key to the Digital Journey

Business leaders may have diverse objectives and may not be naturally inclined to support digital initiatives without a fleshed-out understanding of how digital will impact their areas. Quantifiable results can take a while to achieve from digital investments, so individual business lines may see the pressing concerns of their quarterly targets as more immediate. Organizations should promote a shared understanding of the importance of digital by educating employees about how digital initiatives will tie into business objectives in the medium and long term. Fis are employing cutting-edge training strategies to generate adoption and support for digital technologies and initiatives.

One institution has implemented a digital adoption strategy to provide guidelines and guardrails for the change management associated with digital transformation. Another runs a rigorous, on-site training academy with distinct tracks, not only for new hires but also existing employees. An executive shared that their institution has enacted a policy of creating training videos for any frequently asked questions—not only for employees, but also for clients, as adoption can be a challenge with both demographics.

According to Aite-Novarica Group’s Q3 digital transformation study, the top three digital strategy goals for FIs in 2022 are increasing operational efficiency, delivering a modern CX, and reimagining customer service channels and delivery. In order to deliver on these, FIs need strong digital foundations. Establishing clear ownership is a key pillar of digital transformation and a process that should involve IT and business leaders alike.

How You Can Get Involved

The agenda of each quarterly meeting is driven by Research Council members’ interests and priorities. On December 1, we’ll meet to discuss IT budgets and projects for 2023. Click here to learn more about our Q4 2022 meeting.

Interested in learning more about how our strategic insights can help you navigate departmental- or enterprise-level digital transformation? Email me at mwein@datos-insights.com, and I’ll reach out to schedule an introductory conversation about the Research Council and our FS CIO/CTO Advisory Practice.

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