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Headless Banking and Banking-as-a-Service: Shaping the Future of Finance

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Headless Banking and Banking-as-a-Service: Shaping the Future of Finance

There are two transformative models currently reshaping how financial services are developed, delivered, and consumed across the globe – headless banking and Banking-as-a-Service (BaaS). From North America’s robust financial ecosystems to APAC’s innovation hubs, and EMEA’s regulatory frameworks to Latin America’s burgeoning fintech scene, understanding both of these models is crucial to navigating the scene effectively. Headless banking, where banks separate their front-end and back-end processes to enable greater flexibility and customization in customer interactions, contrasts with BaaS, where banks or licenced institutions offer their banking services to other (usually non-financial) businesses, allowing them to integrate financial services directly into their offerings. And when it comes to the risk decisioning software that supports both of these models, the challenges and opportunities that headless banking and BaaS provide can help to inform our approach to managing and mitigating risks – an approach that needs to innovate and evolve as rapidly as the regulations and new advancements that the industry offers.

Key Features and Benefits of Headless Banking

With headless banking, the traditional integration of front-end user interfaces and back-end banking processes is uncoupled. Taking digital, front-end experiences away from the core banking functions that happen in the background enables an unprecedented level of flexibility and customization, allowing financial institutions to easily (and quickly!) integrate new technologies and services, offering personalized experiences that are tailored to individual customer needs and preferences. This ensures maximum agility and helps to foster rapid innovation, allowing you to get ahead in a highly competitive market by more rapidly adapting to emerging trends and changing customer demands.

Additionally, headless banking can significantly impact your cost efficiency. By utilizing APIs to connect disparate systems and services, you can reduce the need for an extensive overhaul of the backend every time the front-end technology evolves or customer expectations change. This modular approach reduces development and maintenance costs, but also strengthens security protocols, enabling potential security breaches to be isolated and managed more effectively, and minimizing overall risk.

When it comes to regulatory compliance, headless banking offers an adaptable framework that simplifies the integration of compliance measures into both existing and new products. This flexibility is crucial for global expansion, and embracing open banking standards, allowing you to easily customize and localize your offerings to meet specific regional regulatory requirements as well as cultural preferences. The opportunity to expand your global footprint while enhancing your service delivery will help to drive business growth and customer satisfaction.

Key Benefits of Headless Banking

  • Customer-Centric Approach
  • Digital Integration
  • Customization and Personalization
  • Agility and Innovation
  • Cost and Operational Efficiency
  • Enhanced Security
  • Regulatory Compliance and Open Banking
  • Global Expansion and Localization

Banking via Non-Banks: Banking-as-a-Service (BaaS)

Banking-as-a-Service (BaaS) is a model that allows non-banking entities to offer financial services and products by leveraging the existing infrastructure of established financial institutions. While this approach has many benefits for the non-banks (and consumers) who wish to use it, it all hinges on the use of APIs that connect third-party companies (fintechs, retailers, tech giants) directly to the extensive banking services and systems of more traditional financial institutions. BaaS platforms are technically the intermediaries, facilitating seamless integrations between the banks and the non-banks, enabling those non-banks to offer a range of financial services and products (including payments, lending, insurance, and investment services) under their own brand that enable a frictionless, all-in-one experience for their customers.

The operation of BaaS through APIs ensures the basic functionality of ‘banking’ but it also offers the compliance and security that consumers expect when they interact with any sort of financial service or product. BaaS promotes extensive fintech partnerships, giving the opportunity for non-banks to design and deliver truly customized financial solutions that meet the (often very) specific needs of their customer base, without dealing with the burden of developing, maintaining, and regulating a complete banking infrastructure.

BaaS is often seen as a transformative model in the financial services industry, democratizing access to financial services and encouraging innovation and inclusion. Companies can diversify their offerings, improve customer engagement, and generate new revenue streams, all while relying on the track record of robust, secure, compliant banking frameworks and infrastructure provided by established banking partners. Ultimately, while this improves customer access to a variety of financial offerings, it also helps drive competition and innovation in the industry as a whole, enabling more choice and more personalized, frictionless experiences for consumers.

Both headless banking and BaaS emphasize the industry-wide drive towards more modular and flexible financial service delivery, yet technically they both serve as distinct functions within the larger ecosystem. While different in how they function and the infrastructure required, the two models can complement each other quite well. For example, a BaaS provider could adopt a headless approach to provide more customizable interfaces for clients, combining robust back-end services with more tailored front-end designs.

Challenges and Barriers: Overcoming Obstacles in Headless Banking and BaaS

As with any innovation, incorporating headless banking or BaaS into your service offerings can come with unique challenges.

  • Legacy Systems: One of the most significant challenges facing the adoption of any newer technology in the financial services world is the integration of modern, flexible banking models/systems with outdated legacy systems that are often rigid and complex. These systems can make it extremely difficult to implement the agile and modular structures required by headless banking and BaaS (and often can’t efficiently handle the rapid changes to these products that the market and consumers demand). Additionally, it’s imperative to ensure that new, more modular systems can effectively communicate and operate with existing banking systems and third-party services, which can be challenging, especially when dealing with a wide range of standards and technologies. 
  • Strategy: To overcome the challenges of legacy technologies, adopt a phased approach to modernization, gradually replacing or encapsulating legacy components with microservices and APIs that offer greater flexibility. Ensure you have access to a Professional Services team or consultants that have deep expertise in systems migrations.
  • Regulatory Hurdles: Both models must navigate a complicated framework of financial regulations that vary by region and jurisdiction (and often industry – for example, if you’re offering any sort of banking services to the healthcare industry or government entities, there can be an added layer of compliance considerations). BaaS especially faces compliance challenges, as it involves third parties offering financial services – due diligence for regulatory oversight is key.
  • Strategy: Early and ongoing engagement with the right regulatory bodies is critical, and the use of regulatory sandboxes for testing can be helpful. Leverage expert legal and compliance teams to build and maintain a framework that adapts to regulatory evolution and new compliance demands.
  • Security: Security is crucial, as it is with all financial services, especially given the increased exposure to fraud and cyber threats that come with opening up banking systems through APIs and enhanced customer touchpoints.
  • Strategy: Adopt robust cybersecurity measures, including end-to-end encryption, regular security audits, and compliance with the most stringent international security standards to mitigate these risks.
  • Cultural/Organizational Resistance: Shifting to a new model requires buy-in across the organization and often necessitates a significant cultural shift, moving away from more traditional banking practices to more innovative, technology-driven approaches.
  • Strategy: Leadership needs to champion the change, and implement comprehensive training programs to ensure alignment company-wide. Be sure to illustrate the competitive advantages and potential for improved customer experiences and sustainable growth to help gain buy-in.
  • Integration Complexity and Lack of Expertise: While APIs facilitate integration between systems, managing a large amount of interfaces between the front and a variety of back-end services can quickly become complex.
  • Strategy: It can take significant effort to ensure stability, performance, and consistency across all of these interfaces – the key is deep expertise in integrating systems, tech migrations, and developing new infrastructure.

Leading the Headless Banking and BaaS Evolution

Who’s leading the charge? Those organizations who embrace agility, technological prowess, and new models for delivering financial services. Here’s a closer look:

Fintech Startups
Fintech startups are often the most aggressive in adopting headless banking principles due to their digital-native foundations and lack of legacy infrastructure. They are known for their rapid innovation, customer-centric designs, and use of modern technology stacks, making them natural leaders in this space. Examples include:

  • Challenger Banks: Digital-only banks like Revolut, Monzo, and N26, who can leverage headless architectures to offer innovative, user-friendly banking experiences.
  • Banking Platforms: Companies like Plaid and Stripe provide API-driven services that enable other businesses, including traditional banks, to offer fintech solutions seamlessly integrated with their existing offerings.

Traditional Banks
Banks that are investing in digital transformation initiatives, forming partnerships with fintech companies, or developing in-house solutions to modernize their banking platforms. Examples include:

  • Global Banks: Some of the world’s largest banks, such as JP Morgan Chase, Goldman Sachs (with its Marcus brand), and HSBC, are investing heavily in digital banking initiatives, including the adoption of headless and API-driven architectures to enhance customer experiences and expand their digital offerings.
  • Regional and Community Banks: Smaller banks are increasingly partnering with fintech and BaaS providers to leverage headless banking capabilities, allowing them to offer competitive digital services without the need for extensive in-house technology development.

Technology and BaaS Providers
Technology companies and BaaS providers offer the infrastructure, platforms, and tools that enable both fintech startups and traditional banks to implement headless banking solutions. These providers are crucial enablers of the trend, offering the APIs, development platforms, and cloud infrastructure necessary to build and scale headless banking services. Key players include:

  • BaaS Platforms: Companies like Solarisbank, Banking Circle, and Galileo offer banking-as-a-service platforms that enable other businesses to launch financial products quickly and efficiently using headless principles.
  • Cloud Service Providers: Major cloud providers such as Amazon Web Services (AWS), Google Cloud, and Microsoft Azure offer the infrastructure and services that support the scalability, security, and flexibility required for headless banking.

Future Outlook

At Provenir, we’ve been on the forefront of tech innovation for financial services for the past twenty years – and it looks like the next twenty hold just as much potential! So what does the future hold for both headless banking and BaaS? Both models are set to significantly influence wider industry dynamics, driving further transformation in financial services, enhancing customer experiences, and driving operational efficiencies. Some key things to keep in mind:

  • Technology and Innovation: Cutting-edge tech (like AI/ML) is crucial. These technologies enable more personalized banking experiences, less friction in the customer experience, improved security measures, and greater operational agility. Integrating artificial intelligence allows for smarter, data-driven decisions that can be scaled as needed, transforming customer touchpoints to more meaningful, tailored experiences.
  • Seamless Integration: Headless banking and BaaS can help encourage more seamless integration of financial services into the daily lives of consumers, enhancing the customer journey with BaaS services like integrated payments, lending, and insurance. Headless banking will empower more banks to rapidly innovate and customize offerings, reducing the time-to-market for new products and services, and reducing friction along the journey.
  • Cloud-Based Platforms: Holistic, end-to-end, cloud-native risk decisioning platforms can play a pivotal role in the tech transformation of the industry. Platforms like Provenir’s AI-powered decisioning solution can provide the necessary infrastructure to manage vast amounts of data security and safely, and comply with regulatory requirements, while supporting real-time risk assessment and decisioning across fraud and credit.
  • Traditional Bank Response: Prominent tech companies and fintechs are leading the way in BaaS, while larger players like Stripe and Square are providing platform services that enable other businesses to offer financial services. Financial institutions like DBS and BBVA are delving into headless banking by separating their customer-facing interfaces from core banking services. More traditional banks are increasingly responding to these trends by either partnering with fintechs, investing in their own BaaS or headless banking solutions, or acquiring promising tech startups to bring these innovations in-house.

The BaaS market size is estimated at USD 5.32 billion in 2024, and is expected to reach USD 14.72 billion by 2029, growing at a CAGR of 26.60% during the forecast period (2024-2029).

As with all tech trends in the past twenty years (remember your first mobile payment?), anything truly innovative is poised to fundamentally change how financial products and services are developed, delivered, and consumed. As the focus on consumer experience continues to grow, tech that shifts the industry towards more integrated, customer-centric, frictionless experiences will be golden – especially because it can also improve operational efficiency and encourage sustainable business growth.

For more information on how holistic, end-to-end decisioning can help you launch your headless banking or BaaS products:

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Key Benefits

  • Fast and reliable credit scoring. With scoring from Sergel, you can feel secure when you provide credit to both companies and consumers. We provide seconds-quick answers so you can sell immediately.
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Sergel – From Purchase to Payment

Fully Automated.

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Profitability in Focus.

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Advanced Logic.

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About Sergel

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    • Credit Decision
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Bankflip

Employment, Incomes and Debt Data Access in Real-Time

Key Benefits

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  • Increase conversion while cutting your costs down. By adding our solution to your application/underwriting/subscription flows, you will get all the necessary documentation and data needed in just one step (making your conversion going up) and you will be able to directly use it within the Provenir solution to automate the whole process with no humans involved.

Frictionless Data Capturing and Processing

Bankflip is the tech solution to seamlessly collect employment, incomes, debt/risk, and other relevant data of your end-users to supercharge your products and solutions.

Bankflip services allow your company to collect user financial data on a permission basis. It has been designed with a special obsession on end-user experience (UX) and developer experience (DevX) to ensure the best possible usability and the easiest integration.

Supercharge onboarding for your banking app, underwriting for a loan or a mortgage, employment verification for your KYC, among other use cases with real-time, standardized and reliable data directly collected from the private area of Public Authorities.

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About Bankflip

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    Automatically collect all the documents you need in only one connection (cross-authority authentication)

    Verified by default: documents are collected directly from the source

    Documents converted into data (JSON)

    Manual documents uploaded are tagged, verified and converted into data

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Kueski Selects Provenir to Power its Aggressive Growth and Expansion Plans

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Kueski Selects Provenir
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Provenir and Kueski partner to help eliminate financial exclusion in Latin America

Mexico City — June 13, 2022 — Provenir, a global leader in AI-powered risk decisioning software for the fintech industry, today announced that Kueski, one of the largest buy now, pay later (BNPL) and online consumer lenders in Latin America, selected Provenir’s AI-powered Risk Decisioning Platform to fuel its aggressive growth goals for its BNPL offering, KueskiPay.

Kueski’s mission is to make the financial lives of people in Mexico easier by expanding access to traditional financial products and services to millions of underserved, underbanked consumers. This requires fresh approaches in determining a consumer’s ability to pay, such as analyzing alternative data versus traditional credit checks. So far, Kueski has disbursed more than 6 million loans, totaling nearly $1 billion in accumulated disbursed capital. Its collaboration with Provenir will help the platform to continue to grow and consolidate itself as the leading BNPL solution in the LATAM region.

“Today we are using AI, machine learning, and big data, but knew that we needed a more robust, flexible, risk decisioning platform to support our growth and product expansion plans,” said Héctor Cuesta, Director of Product Management at Kueski. “Provenir’s AI-powered Risk Decisioning Platform provides access to more diverse data which will give us deeper insights so we can make the best decision for our customers. Provenir can also be implemented quickly, enabling us to realize these benefits and achieve our business goals.”

Provenir brings together the three essential components needed – data, AI, and decisioning – into one unified risk decisioning solution to help organizations provide world-class consumer experiences. This unique offering gives organizations the ability to power decisioning innovation across the full customer lifecycle, driving improvements in the customer experience, access to financial services, business agility, and more.

“Provenir and Kueski are both committed to using technology to help eliminate financial exclusion in the region,” said Jose Luis Vargas, Executive Vice President and General Manager of Provenir. “With our platform, Kueski can use champion/challenger testing strategies to deploy decisioning models optimized specifically for underserved individuals.”

This partnership will improve Kueski’s data analysis, allow for a deeper understanding of its customers, and further position Kueski as an innovative and disruptive financial services player. Provenir will support Kueski in strategic decision making, and be able to quickly identify, prioritize and execute any next steps, allowing Kueski to focus on its main mission of financial inclusion for all.

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Provenir Named a Gold Winner for AI Platforms in Juniper Research’s Future Digital Awards – Fintech & Payments

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for AI Platforms in Juniper Research’s Future Digital Awards – Fintech & Payments

Company’s AI-Powered Decisioning Platform recognized in the “AI Platform” category

Parsippany, NJ — Oct. 11, 2022 — Provenir, a global leader in AI-powered risk decisioning software, today announced that its AI-Powered Decisioning Platform has been named a gold winner in the “AI Platform” category in Juniper Research’s Future Digital Awards – Fintech & Payments.

The Fintech & Payments awards program honors the very best technologies across the fintech, payments, fraud and security, banking and blockchain sectors. Since 2008, the Juniper Research Future Digital Awards have been awarded to technology companies at the forefront of their respective fields: companies that deliver imaginative, innovative products or services that have the potential to disrupt their ecosystems and provide significant benefits to their target audience. 

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According to a recent study, AI-enabled risk decisioning is seen as key to usher in improvements in many areas, including fraud prevention (78%), automating decisions across the credit lifecycle (58%), improving cost savings and efficiency (57%), more competitive pricing (51%) and improving accuracy of credit risk profiles (47%). The study also revealed that 55% percent of respondents who plan to invest in an automated credit risk decisioning system consider AI to be one of the most important features.

“Provenir is honored to be named a gold winner for AI Platforms in Juniper Research’s Future Digital Awards and to be among a prestigious group of innovative technology award winners advancing the fintech and payments sector,” said Kathy Stares, Executive Vice President Americas at Provenir. “Provenir’s AI-Powered Decisioning Platform delivers a comprehensive real-time view of decisioning performance, easy access to third-party and historical data, as well as automated and auto-optimized AI, enabling organizations to deliver intelligent decisioning needed to grow their business.”

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On-Demand: How Consumer Lenders Can Reduce Friction Without Compromising on Risk and Fraud Prevention

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Customer experience is incredibly important to today’s discerning consumers, whether they are looking for financial services or any other product. Reducing friction at onboarding and across the entire customer journey is critical for consumer lenders – but how can you do that without compromising your risk strategy or increasing your risk of fraud? Watch on-demand now, and hear from our panel of experts who share insights and best practices for reducing friction, so you can effectively balance risk with opportunity – and grow your business.

Key highlights include:

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Maximizing AI/ML for Fraud and Risk Mitigation

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Maximizing AI/ML
for Fraud and Risk Mitigation

  • Jason Abbott, Senior Product Manager, Fraud Solutions
  • May 6, 2024

How to Harness Artificial Intelligence and Machine Learning for Comprehensive Fraud Protection

The battle against fraud and risk in financial institutions is complex, and it’s always changing. And fraud doesn’t start and end with the onboarding of applicants – it’s a continuous challenge that demands evolving strategies. This is why it’s critical to look at risk decisioning solutions, including artificial intelligence and machine learning, that can access real-time data across the journey – tackling fraud screening not just at the application stage, but throughout the entire customer lifecycle.

Real-time data for real-time decision making

Artificial intelligence and machine learning (AI/ML) play a pivotal role in detecting and preventing fraudulent activities. With financial fraud methods becoming more and more sophisticated, one key way to stay ahead of fraudsters is accessing real-time data, integrating it into your risk decisioning solutions, and automating the use of that data with AI/ML. In this way, you can react swiftly (and accurately) to ever-evolving fraud threats. 

But it’s critical to balance fraud mitigation with the customer experience. While admittedly powerful technology, AI/ML requires more than just advanced algorithms and risk models – it needs a comprehensive understanding of the overall decisioning operations, customer experience, and the regulatory and compliance landscape of financial services organizations in the regions you operate. An effective fraud decisioning model needs to not only intercept fraudsters, but it needs to be sure that it doesn’t introduce more friction for legitimate customers. Tightening the net on fraudsters isn’t the most optimal answer – we need to ensure that embedded intelligence is working efficiently to keep out the bad actors while still extending the right products and offers to a growing number of creditworthy customers.

Intelligent use of data throughout the customer journey

A common challenge that financial institutions face is the underutilization of valuable customer data that gets collected during the application process. Rather than discarding this data, it should be integrated into ongoing monitoring programs and used to enhance risk mitigation strategies, especially during high-risk events. For example, take the case of mule account detection, where initial application data contains the right indicators that help approve an applicant. But with ongoing monitoring as new data becomes available, financial institutions could intervene later if new suspicious activity is tracked. With a set-it-and-forget-it mindset and the lack of ongoing monitoring, fraudsters can more easily slip through the cracks. As fraud methods become more evolved, the risk models needed to prevent fraud need to evolve as well. Many times, actors with ill-intent will use legitimate credentials to gain access to products and services and then pull a bait-and-switch when onboarded. Without the use of ongoing monitoring and the continuous intelligent, optimized use of risk data across the journey, these sorts of situations become difficult to catch until it’s too late. 

This is why adapting quickly to new threats is so critical. Flexibility and responsiveness are key things to look for in a fraud/risk decisioning solution, because with the adaptability to add new data sources, optimize risk models based on intelligence, and change decisioning processes easily, you are able to respond to threats more effectively. AI/ML models act like the central nervous system of a modern sports car, where every component must communicate and function in unison to effectively respond to changing conditions – in the case of a car it’s road conditions, weather conditions, engine temperature, etc. In the case of fraud mitigation, you need to ensure that you can adapt quickly without being bogged down by manual processes or IT backlogs to make changes.

Efficient data integration

Not all financial institutions have the ability to integrate extensive datasets into a smart, unified model or data lake. Whether it’s technical restrictions, resource issues, IT backlogs, or the challenges of merging disparate systems, there are many factors that can hinder efficient data integration. What’s needed is an effective fraud orchestration layer, combined with low-code or no-code capabilities, allowing you to adapt and innovate as quickly as threats do, giving you a significant competitive advantage (and again, helping to maintain a positive customer experience with limited friction). 

So what are the key things to consider when it comes to enhancing your fraud mitigation strategy by harnessing AI/ML? Think of the following:

    • Does your AI/ML model for application fraud provide reliable scoring and clear explainability?

    • Can you integrate fraud-rich data into your application fraud infrastructure?

    • How easily can you integrate new data sources in response to emerging fraud trends?

    • Are you able to leverage available data to address potential post-application fraud?

    With cutting-edge technology designed to empower financial institutions to not only respond to threats in real time, but also anticipate them before they can cause harm, decisioning technology that incorporates robust AI/ML solutions will ensure your organization (and your customers) remain secure and satisfied.

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    • AI-powered Datasets and Adverse Media Screening. Comprehensive, watchlist datasets and adverse media, collated using proprietary, gold-standard name matching logic and maintained using AI-powered technology designed to cover all diligence and risk management use cases. It is built for easy integration with packaged software and customers’ internal platforms alike..

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    This tool serves as the foundation for our sophisticated analytical solutions, integrating cutting-edge AI, all while upholding ethical standards and respecting privacy within the regulatory framework. Our solutions empower you to exercise greater control with automated customer data enrichment and seamless data automation through our automatic data pipelines. We also offer innovative modeling and scorecards, in addition to assisting your team in developing customized scorecards. By embracing these solutions, you can embrace the future of financial inclusion and cater to the unique needs of the next generation.

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      Adverse Media: Our dataset aggregates information from various sources, including media outlets and public records, documenting negative events or controversies involving individuals, entities, or organizations.

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      • Global

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    Successful Digital Transformation in Financial Services

    Q&A with Industry Experts

    Successful Digital Transformation
    in Financial Services

    • 01

      Digital transformation is critical for financial services organizations who want to thrive in our increasingly digital age. As consumers demand more and more from their financial services interactions, those organizations that don’t evolve will be left behind. But what are the keys to a successful journey?

      We recently hosted a webinar focusing on the intricacies of this transformative process, looking at key challenges and guidance for financial institutions looking towards a digitally empowered future. And from that discussion, a number of insightful audience questions were addressed – so we wanted to share some of them here with you!
    • 02

      With digital transformation came digital banking which made life easy for both consumers and would-be thieves. How can we mitigate the increasing hacking risks associated with digital  banking, from both the customer side and the bank side?

      Digitalization is increasing – and yes, so is fraud. This is where client authentication becomes so important, and truly understanding and knowing your customer is key. Device authentication for example can be critical, as well as collecting the required data to be sure we are understanding our customers without impacting the customer journey. That level of discipline needs to sit with the financial institution, without necessarily being seen or experienced by the customer. Identity theft is quite prevalent, especially in certain regions like the Nordics, so it’s critical to balance the need and desire to have strong fraud and identity management in place, without adding friction to the process for your consumers.

    • 03

      How is generative AI impacting decisioning?

      There is a potential for a large impact on decisioning with the use of generative AI tools. We’re in the early adoption stages, because from a regulatory and compliance standpoint, there is a nervousness about using these tools to push businesses forward. Institutions are risk averse, cautious, and measured in the terms of the policies they implement. Corporate governances are challenges for many banks, particularly when dealing with a variety of regional regulations. In part, it comes down to explainability. While AI tools can certainly help from a risk decisioning standpoint, and should be fully explainable in that regard, there is not enough known about the control and regulation of generative AI tools to ensure that data is being used and stored properly. Ultimately, we’re early on in this journey and they will play a fundamental role in our industry over the next few years.
    • 04

      What is the importance of being able to adjust business lending and fraud rules quickly given the rate of change in the macro-economic landscape, customer behavior and MO of fraudsters? Why are organizations, particularly in the financial services industry, struggling to keep up with these rates of change?

      Often, organizations struggle to keep up with the rate of change due to the technology infrastructure in place. Being able to make changes quickly to respond to market demands and evolving threats is key to not only accurate fraud prevention, but also simply ensuring that you’re meeting the needs of your customers. If you have to wait six weeks for sign-off on a policy change, and then wait additional time for a vendor or your IT team to make iterations in your decisioning processes, you’ve left your organization susceptible. Having self-sufficiency in times like these is critical – being able to use advanced analytics to optimize decisioning strategy, quickly, and then make those changes just as quickly is key. But you need the right technology in place to support that flexibility and agility.
    • 05

      When the bank is undergoing a full digital transformation, many projects and developments are done at the same time with limited resources. What does management need to pay attention to when making decisions on priorities?

      The first step is making sure that all projects have been categorized and prioritized with the entire group, and that those priorities are aligned with the overall group/organizational strategy. Alignment is key. It is very difficult to have competing projects fighting for resources (time, money, human) and this is a common challenge among financial services institutions. Allowing for a level of flexibility and adaptability is crucial – often what helps is reevaluating priorities at set intervals, every quarter for example. The largest priorities may not change often, but the smaller, more nimble priorities can (and often do), and your project management structure should be flexible enough to accommodate that.
    • 06

      Given the increasing flow of information, number of processors and variety of processors within the competitive landscape, what is the importance of increasing the number of data connections to enhance decisions towards better business outcomes?

      Increasing data connections can be helpful, but it’s worthwhile to note that we don’t want to connect to so many that it’s overwhelming. It’s not just about more data, it’s about the right data at the right time, in order to see the real value of those data sources. Getting the right level of customer data that you need to adequately support your decisioning processes is crucial. Having a broader spectrum of data available, in terms of types of data sources and variety, as well as quality, is more important than just continually adding new data sources that won’t provide any additional value to the view of your customers (and may in fact add more friction to the journey). Data sources that allow for a strong level of automation in your decisioning processes will also be more valuable than those that require manual intervention or human oversight (which add complexity and slow down the process).
    • 07

      Will the current path of digital transformation that banks are on (locally and globally) lead to more financial stability or more future crisis scenarios (like Silicon Valley Bank)?

      Financial stability is important – we weathered this during the financial crisis in 2008, and there are continual efforts to combat any instability. One of the things that led to that instability is the fracturing of the value chain. When you have new players who are so specialized and who don’t see the whole banking picture, there are inherent risks. On the other hand, when you have large incumbents who do everything in-house, they see the whole picture, but they can often be very rigid and slow to move or make changes, which has different risks and implications on financial stability.

    Balance risk with opportunity across the customer lifecycle.

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