Who’s Shaping the Future of Banking? The Leading 50 Core Tech Providers
Banking enterprises today are focusing on driving the velocity of change ⚡, managing cost takeout requirements 💸, and increasing resiliency in operations 🔧 as they look for the right set of technology providers 🖥️. This session will help banking buyers navigate the complexity 🧩 of the modernization of decisions, diverse options 🌐, and a wide array of technology providers.
Discover the leading 50 core banking technology providers 🏆 spearheading modernization and the future of banking for BFS enterprises 📊. These providers offer innovative core banking platforms designed to boost efficiency 📈, streamline operations 🛠️, and elevate customer experience ⭐.
Watch us explore key trends and innovations shaping the banking industry 🌍 and learn what sets these providers apart, from their rapid innovation 🚀 and capacity to demonstrate value through transformative technology 🔄 to their ability to deliver tailored solutions 🎯.
The speakers shared who the leading 50 core banking technology providers are, offer valuable insights 💡 for both banks 🏦 and technology providers 💻 looking to stay competitive in an evolving market, and the emerging role of AI (generative AI 🤖 and agentic AI) & data 📊.
During this collaborative LinkedIn Live session, we explored:
• Who the leading core banking technology providers are 🏅 • What makes the leading 50 stand out in today’s competitive landscape 🌟 and the geographic view 🗺️ • How can data help drive personalization at scale 📈 • How cloud ☁️ is transforming the banking industry 💼 • What role are generative AI 🤖 and agentic AI expected to play 🎯
After witnessing a hazy macro environment for five straight quarters, India-centric IT services firms saw green shoots in the first quarter of this fiscal. While experts were still uncertain about the continuity of tech spending, Q2 (July to September) earnings belied the doubts of the naysayers.
Peter Bendor- Samuel, CEO of Everest Group, said that the BFSI sector is witnessing a rebound following the post-COVID slump. “BFSI typically leads other industry segments, so this is an encouraging sign for the rest of the IT industry,” he said.
India’s IT industry appears to be staging a recovery from a deep slowdown, driven by an increase in discretionary spending, particularly in the financial services vertical and the North America market.
The optimism expressed by leading Indian IT firms is mirrored by analysts, who see an uptick in hiring and upward guidance revisions as indicators of a brighter outlook ahead.
“We are witnessing a rebound following the post-COVID slump. Early signs emerged earlier this year, and now there’s significant conviction that this recovery is real,” Peter Bendor-Samuel, CEO of consulting and research firm Everest Group, said, referring to the BFSI sector. “BFSI typically leads other industry segments, so this is an encouraging sign for the rest of the IT industry.”
Operations outsourcing is evolving across major industries as rapid technological advancements and changing customer expectations reshape traditional models. What once focused on cost arbitrage is now transformation-centric, driving agility, enhanced efficiency, and improved customer experience. A 2024 key issues survey by Everest Group highlighted that beyond cost optimization, the top three key areas where enterprises seek direction include technology integration, advanced analytics, and process engineering. The BFS industry is at the forefront of this shift toward transforming operations.
Cost-arbitrage led transformations are now becoming a thing of the past, whereas enterprises are now increasingly focusing on process efficiency through reengineering, technology-driven operations, and analytics integration. Future-looking enterprises are already taking this a step further, viewing these advancements as enablers of additional business value and enhanced customer experience, rather than just cost savings.
In this blog, we explore this growing trend toward operational transformation and how financial institutions with varied enterprise maturity levels are all striving to align with this shift.
What is the operational transformation approach and why are enterprises adopting it?
While a few banks were ahead of the curve in making operations more integrated with technology, the number of banks accelerating the efforts has surged in the past three years following the COVID pandemic and amid the slowdown. Some of the factors contributing to this include:
Increasing requirement for automating business processes through remote delivery models across organizations
Heightened market regulations driving the transition to shorter settlement cycles
Accelerated demand from end customers to shift to digital models
Need to scale up quickly to match capabilities with new players in the market, such as FinTechs and non-banks
Need for low-cost IT infrastructure and data accessibility
Enhanced data privacy and security
Unlike pure-play operations outsourcing, which primarily targets cost reduction, an operational transformation strategy focuses on optimizing workflows to foster organization-wide synergy and achieve long-term, stable gains. While every organization takes a unique approach to operational transformation, these efforts can be crystallized into three key approaches:
Operations-IT alignment: Synchronization of IT investments in line with organizations’ wider business goals
Data & Intelligence (D&I) integrated workflows: Leverage of automation, AI-, and analytics-led solutions to make operations faster and intelligent
Platform-led operations: Deployment of domain-centric platforms for automated processing of large volumes of transactions
Through these dimensions, enterprises are seeking to transition their current stateof operations into a more sustainable, integrated state of operations in the coming future. Exhibit 1 highlights the current and aspirational state of financial institutions.
Exhibit 1: Current vs future state of enterprise transformation approach
Table 1: Pros and cons of the siloed and integrated states
Current state: Siloed
Future state: Integrated
Pros
Cons
Pros
Cons
Cost arbitrage opportunities
Lower upfront investment, however, with limited long-term value creation
Siloed structure
Misaligned Objectives and Key Results (OKRs)
Tech & ops model changes out of sync
Lack of seamless customer reach
Shared OKRs
Singular focus on business growth and outcomes
Maximum domain-centric synergy
Seamless customer reach
Coordinating multiple teams to align on shared OKRs requires extensive change management efforts to ensure smooth collaboration
Even though integrated operations warrant higher initial investment, enterprises are gradually adopting this model as their focus shifts from short-sighted goals of immediate cost takeout to long-term return on investments. This focus shift is explained by three key questions that enterprises are increasingly seeking to address:
How to drive the velocity of change?
Financial institutions are striving to accelerate change velocity to address evolving macro, consumer, competitive, and regulatory trends. However, in the current state, this change becomes slow due to siloed teams that are focused on individual outcomes and key results (OKRs). In contrast, integrated, technology-infused operations promote shared, forward-looking goals, facilitating faster change. As organizations recognize this, more data and intelligence-centric outsourcing deals now involve business unit heads alongside CIOs as decision makers, ensuring greater domain and process synergy.
How to bring in additional value while reducing the Total Cost of Ownership (TCO)?
As an organization’s wider goal is to reduce the overall cost, enterprise stakeholders are gradually moving away from the thought process of bringing down the cost of individual operations, IT, and technology teams to optimizing the total cost of ownership. For instance, while standalone operations outsourcing was the traditional answer to quick ROI, banks are now following a two-pronged approach of automating transaction-intensive functions and outsourcing judgment-intensive functions to gain to a stable, long-term ROI.
Along with reducing TCO, an integrated approach unlocks business value by using data, analytics, and AI to enhance decision-making, uncover customer insights, and drive new revenue streams via personalized service offerings.
How to make business operations more resilient?
Following the pandemic, resilience has become one of the top priorities for BFS firms. Institutions are aiming to safeguard against macroeconomic and regulatory changes and build operations that are agile, scalable, and secure. Thus, banks are looking to switch to a model where technology adapts to external fluctuations and helps minimize operational disruptions.
Is the time right to prioritize transformation? Case in point:
The case of traditional UK banks provides a compelling answer to why operational transformation is imperative in today’s scenario. Compared to fintechs, traditional banks in the UK were laggard in adopting digital operations, particularly within CX-centric functions, in the past decade. As customers became more digitally savvy, fintechs were quick to offer seamless, online banking experiences. As a result, many customers shifted to these digital-first alternatives. By the first half of 2023, neobanks such as Revolut and Monzo were nearly at par with established banks in terms of customer base. The past two years witnessed over 300 physical branch closures of traditional banks due to customer shifts to digital services. In response to the significant shift in customer expectations, traditional banks have been forced to rethink their approach, accelerating digital adoption to stay competitive.
How to set the wheels of progress in motion? An operational transformation toolkit for BFS enterprises:
The following section entails a self-assessment framework for enterprises (in Exhibit 2) to evaluate their current operational transformation maturity and select a sourcing approach accordingly.
Exhibit 2: Operational transformation readiness assessment framework for BFS enterprises
Enterprises need to optimize prioritization of initiatives based on their readiness stage to ensure the most impactful transformation outcomes. This is highlighted in Exhibit 3 below.
Exhibit 3 (a): Outcome prioritization based on the transformation readiness of BFS enterprises
Exhibit 3 (b): Varying objectives across broader operational and infrastructure layer
Based on the stage of operational and infrastructure maturity, an organization can prioritize the degree of transformation feasible. For instance, Fundamental-state enterprises can begin by introducing business intelligence and RPA tools for select processes, while mature-state enterprises can start leveraging technologies such as advanced analytics and end-to-end automation platforms.
How leading banks are navigating their transformation journey:
Based on their operational maturity and infrastructure compatibility, banks are selecting from three main approaches: Data and Intelligence (D&I)-integrated operations, the deployment of technology platforms, and aligning operations with IT.
Examples of such initiatives in the past three years are mentioned below:
A leading UK bank:
With an aim to optimize cost and enhance process efficiency, a UK-based retail bank roped in a third-party provider to leverage its services for IT and support functions along with digitally transforming its business. As part of the engagement, the bank will leverage the provider’s automation and AI capabilities to accelerate transaction processing and enhance customer experience.
A leading Europe-based global bank
To streamline customer-facing workflows and enhance the lending experience, the bank partnered with a BPO provider to identify and automate processes across multiple functions and leverage AI for quicker loan processing. Consequently, the bank achieved a 25% increase in productivity and reduced account closure times by approximately 30%.
A top 5 mortgage-backed securities dealer
Faced with an outdated proprietary technology platform that couldn’t support T+2 settlement cycles, the firm turned to a third-party provider for both technology and services. The provider took over post-trade operations while facilitating the firm’s transition to a modern equities platform. This partnership enabled the firm to scale its operations effectively and meet regulatory requirements.
Conclusion:
The decision to transition to an integrated operations approach depends on whether an organization prioritizes short-term cost-cutting or long-term efficiency. While both approaches offer distinct advantages, adopting a strategy focused on sustainable growth equips enterprises to withstand external disruptions and maintain their relevance in an evolving market. Given the significant investments required, although justified by the strong ROI and the complexity of internal execution, partnering with a third party offers access to production-ready and proven solutions, a skilled talent pool with domain and technical expertise, and cost-effectiveness to help enterprises achieve their goals. For questions or to explore this topic further, reach out to us.
Artificial intelligence is evolving faster than a quarterly earnings report, and just when we’ve started to master generative AI , a new breakthrough is emerging: agentic AI!
This isn’t just another buzzword to add to your corporate lexicon either—it’s a game-changer that’s set to redefine AI’s capabilities.
Agentic AI is an evolved form of AI that creates autonomous agents possessing autonomy, decision-making, and adaptability. The agents can execute tasks in their entirety through natural language-based inputs. They can also set goals independently, plan accordingly, and act to accomplish the targets. Key characteristics of agentic AI include:
Autonomy: perform tasks independently
Reasoning: make advanced decisions
Flexible planning: adjust plans based on prevailing circumstances
Natural language understanding: comprehend and follow complex instructions
Continuous improvement: learn from historical data and feedback
System integration: integrate with diverse enterprise systems
The winning formula for agentic AI is training the models on diverse datasets with clear and concise instructions.
What does it mean for the banking and financial services industry?
In Banking and Financial Services, agentic AI could be the key to optimizing operations, automating complex processes, and delivering hyper-personalized customer experiences.
Agentic AI assesses the need for actions before executing them and continuously learns from its experiences to improve decision-making.
Now let’s dive into why this innovation is catching the attention of technology and financial leaders and how it could now transform the financial services industry.
In the fast-moving world of trading and investment , agentic AI has the potential to transform portfolio management. These AI agents can analyze market trends, make rapid trading decisions, and adapt investment strategies in real time based on economic data and news events.
Beyond trading, agentic AI could enhance risk management by autonomously identifying potential market disruptions or regulatory changes and adjusting exposure accordingly. In personalized banking, it could optimize customer service, offering tailored financial advice, automated portfolio management, and fraud detection systems that continuously learns and adapts by the second.
This combination of real-time decision-making and autonomy could lead to more efficient markets, improved risk mitigation, and potentially higher returns for investors and clients alike.
What are the high priority use cases for agentic AI in banking and financial services?
Agentic AI is a transformative force driving exponential growth for banks by revolutionizing customer engagement, decision-making, and operational efficiency.
With its ability to incorporate a “chaining” capability in decision making, banks can deliver hyper-personalized products and services, significantly boosting customer loyalty and unlocking new revenue streams through targeted cross-selling and upselling.
Agentic AI will empower banks to make smarter, faster decisions on investments and lending, while superior risk management enables more aggressive growth with minimized losses.
The following exhibit highlights the most relevant use cases from a banking and financial services perspective.
Which technology providers are riding the agentic AI wave already?
The vast ecosystem of core banking technology providers, are still familiarizing themselves with the nuances of embedding AI into core baling modules offered via their plaforms. Our conviction is that core augmentation providers, hyperscalers, and niche agentic AI start-ups are going to lead the agentic AI revolution for this industry.
From a core augmentation provider perspective, we see technology platforms in the areas of experience, data & analytics androbotic process automation (RPA) leading the way , in order to guide and augment the core banking platforms, and enabling access to latest technologies.
In recent days, we have already seen the launch of Agentforce by Salesforce that is positioned as suite of autonomous, and personalized assistive agents to support employee’s workflow with specific tasks.
On the other hand, RPA providers are sitting on a base architecture that enables them to manage and automate tasks. Automation Anywhere is offering AI Agent Platform to build its own AI agents, while UiPath is also incoporating these capabilities into its existing RPA offerings.
Additonally, financial crime remains a particularly ripe area for disruption by agentic AI, as technology providers are deploying AI agents to fight financial crime such as WorkFusion.
We also see Google with its Vertex AI Agent Builder and Microsoft with its AutoGen, offering to build AI agents, that provide the necessary frameworks to accelerate agentic AI development.
There are also a few niche providers such as EMA that are catering to use cases for the financial services industry and it will be interesting to see how other firms evolve and adapt in the weeks and months to come.
Potential challenges on the road to adoption
Adopting agentic AI faces several challenges, including high costs and an uncertain return on investment (ROI). Change management and acquiring the right talent are critical hurdles.
Existing technology investments, such as process automation, orchestration, and core modernization efforts, can complicate integration. Additionally, data readiness for training AI models and the substantial effort required to train and integrate these solutions into the value chain are among the other obstacles currently facing firms.
What support do banks and financial service (FS) firm need?
Looking at the technology estate of banking and financial services firms, we see a spider like mesh of various systems and applications that have evolved over the years.
Streamling them to accomplish a workflow, retrieving the right set of data, and arriving at the meaningful insight is no singular feat and one that continues to be amongst the biggest challenges for enterprises today.
Agentic AI can help jump through various of these applications to automate tasks while needing support from other agents to complete the tasks.
Banking and financial services enterprises thus need to ensure their data assets are ready to be uttilized by agents while the workflow and processes are clearly defined. It is on this bedrock that these enterprises will be able to deploy agents.
If you have any questions, would like to gain expertise in Agentic AI and artificial intelligence, or would like to reach out to discuss these topics in more depth, contact Pranati Dave, Ronak Doshi and Kriti Gupta.
The wealth technology industry has recently witnessed significant changes, particularly within one of its leading firms, undergoing a substantial strategic transformation.
This comes in the wake of Adrian Durham, the founder and long-serving CEO of FNZ Group, announcing his decision to step down after 21 years at the helm.
Durham’s departure marks a strategic shift for FNZ and the wealth management industry. As a key figure, this move signals a new direction for FNZ. He will stay on as a non-executive founding director and senior advisor, continuing to contribute his expertise – but what does the future now hold?
Reach out to us to discuss this topic further with our expert analysts.
New leaders take the helm
FNZ’s leadership transition introduces both opportunities and challenges with the arrival of Blythe Masters, a former JP Morgan executive, joining as CEO.
Joining her are Roman Regelman, a former BNY executive, as group president, and Stephen Daffron as strategic advisor. This new, diverse leadership team is expected to drive growth and innovation at FNZ, with a focus on integrating technology to enhance client experiences and operational efficiency.
Masters’ background in investment banking and technology also suggests a strategic shift towards digital transformation, aligning FNZ with broader industry trends within the wealth management industry.
The leadership changes have been announced as FNZ’s existing institutional shareholders, have committed $1bn of capital to support the enduring success of the business over the long term.
Technology trends in the wealth management industry and the opportunity for FNZ
Infusing data and intelligence into wealth operations across front, mid and back-office: Wealth management firms are looking to accelerate the infusion of data and intelligence into their operations, to drive productivity, better stakeholder experiences, and business agility. This places a focus on enabling analytics and artificial intelligence (AI) adoption, by streamlining the business processes, data, applications, and information technology (IT) infrastructure stack
Building customer trust in a hybrid channel model of intelligent self-serve and high-touch advisor model: As the industry undergoes intergenerational wealth transfer, catering to different personas requires a tailored approach to increase adoption of self-serve modules in conjunction with advisors. Wealth management firms thus need to leverage technology with the right balance of human touch and digital touch
Enabling the next-generation advisor experience powered by cloud and AI: AI and generative AI (gen AI) remain key buzzwords with cloud serving as the foundational backbone, to enable production grade availability of these technologies. Advisors need access to the AI-enabled tools that can help streamline their day-to-day workings, so they can focus on serving clients better. As the industry adopts next-generation advisor experiences powered by AI and cloud, FNZ can build on top of its existing machine learning (ML) models to determine the most suitable exchange-traded funds (ETF) / Mutual Funds, and enhance the data search feature that currently looks at multiple documents for relevant data that can be shared with advisors
Access to alternate investment classes and increasing sustainability preferences: Investors and clients are now asking for alternative investment classes and different products to cater to their investment philosophies and visions.
Technology providers are now playing catchup to this unique demand trend that has shades of hyper-personalization. With this going beyond contextualizing experiences, instead bringing a material impact to portfolios.
FNZ has a sustainable finance platform, and it will be interesting to see what innovation happens in this platform area
Cost takeout demand across technology and wealth operations: The wealth management industry is looking at minimizing the total cost of ownership of each value streams, over indexing on the four factors of software, IT services, business process services, and IT infrastructure (including cloud and compute as AI adoption scales).
We have already seen FNZ taking forth the joint value proposition of technology, infrastructure, and operations in a single platform. The cost takeout theme will now continue to take centerstage in this volatile macro-environment.
The wealth management industry is undergoing rapid transformation, and FNZ’s $1 billion investment is an opportunity to capitalize on key technology trends and revolutionize decision-making across front, mid, and back-office functions through AI and analytics.
In the shift towards a hybrid channel model, FNZ can build upon self-serve tools combined with high-touch advisor support
Implications for FNZ and the broader wealth management industry:
We see the following implications and impact coming together for this industry.
Increased investments and innovation will expand and enhance the current portfolio of FNZ’s offerings as outlined above, leading to more sophisticated offerings tailored to wealth management firms’ needs
Penetration into different geographic regions may be on the cards as we see FNZ’s consistent investments via acquisitions, platform launches, partnerships in last 12-18 months. We have already seen the APAC region to be a key focus area for FNZ’s next rung of growth charter as outlined by Asian leadership
Environmental, Social and Governance (ESG) is expected to be a key investment area for the firm going forward, given the customer demand themes and renewed focus on the space
Net-net, the investment of US$1 billion and new leadership puts the organization in a good spot to accelerate product innovation and expand its offerings.
To discuss this topic in more detail, to hear more about wealth management technology and the latest trends or for an even more detailed analysis, please contact Ronak Doshi ([email protected]), Kriti Gupta ([email protected]) and Priyanshi Gupta ([email protected]).
The lending services operations market has experienced significant turmoil in recent years due to high interest rates and other macroeconomic factors, leading to reduced consumer activity. Consequently, providers are continuously innovating to meet local requirements and maintain a competitive edge.
Consumer demand is shifting toward younger, digitally savvy borrowers prioritizing experience and convenience over cost. To serve this evolving demographic, service providers are differentiating themselves by leveraging their partnership ecosystems and recent acquisitions to enhance their capabilities and accelerate time-to-market for new offerings. Investments in digital lending solutions and tools are helping streamline the end-to-end lending life cycle and improve the overall customer experience.
In this report, we examine the lending services operations market and its provider landscape. The report assesses 30 providers and positions them on Everest Group’s PEAK Matrix® framework as Leaders, Major Contenders, and Aspirants. Each profile comprehensively describes the provider’s vision, delivery capabilities, market success, and key strengths and limitations. The study will assist key stakeholders, such as banks, lenders, financial institutions, service providers, and technology providers, to understand the the lending operations service provider market’s current state.
Scope:
Industry: banking and financial services
Geography: global
In this report, we study vertical-specific lending operations. We have not covered horizontal business processes such as finance and accounting, human resources, procurement, and contact center
Contents:
This report features 30 lending services operations service provider profiles and includes:
Each provider’s relative positioning on Everest Group’s PEAK Matrix® for lending services operations
Providers’ market impact, vision, and capability assessment across key dimensions
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
Capital Markets IT Services PEAK Matrix® Assessment
The capital markets industry is transforming to enhance operational efficiencies and reduce costs. Firms are streamlining operations by automating processes, integrating new technologies, and outsourcing non-core activities such as IT management and back-office functions.
There is a significant shift toward emerging technologies. Enterprises leverage AI and Machine Learning (ML) for predictive analytics, automated trading, and personalized financial advice. Blockchain is vital to improve transaction transparency, reduce fraud, and accelerate processing times.
Regulatory compliance is becoming stricter, compelling firms to adopt agile compliance strategies. Enterprises are investing in Regulatory Technologies (RegTech) to manage compliance efficiently across global regulations. Additionally, cybersecurity has become vital, requiring firms to implement robust measures to protect sensitive data and secure digital transactions.
In this report, we analyze 27 capital markets IT service providers featured on Everest Group’s proprietary PEAK Matrix® framework as Leaders, Major Contenders, and Aspirants. Scope:
Industry: Banking and Financial Services (BFS)
Geography: global
This report is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with leading technology and IT services providers, client reference checks, and an ongoing analysis of the capital markets IT services market
Contents:
Examine key trends in the capital markets IT services industry
Classify 27 capital markets IT service providers as Leaders, Major Contenders, and Aspirants on Everest Group’s proprietary PEAK Matrix® framework as Leaders, Major Contenders, and Aspirants
Discuss the IT service providers’ competitive landscape for capital markets IT services in BFS
The PEAK Matrix® provides an objective, data-driven assessment of service and technology providers based on their overall capability and market impact across different global services markets, classifying them into three categories: Leaders, Major Contenders, and Aspirants.
Mastercard has chosen India as the launchpad for its Payment Passkey Service, a new way of conducting online transactions, marking a significant step in the global payments landscape.
“Mastercard’s decision to introduce its Payment Passkey service first in India is a strategic move that aligns with several key market trends and opportunities,” Pranati Dave, Practice Director at Everest Group, said. “India is one of the world’s fastest-growing digital payment markets, with a rapidly expanding user base for online and mobile transactions.”
Getting past the shiny object phase of Gen AI in banking and financial services
September 23, 2024
11:00 AM PT | 2 PM ET
Catch Everest Group Partner Ronak Doshi as he joins an expert panel to explore how to navigate the complexities of AI integration and governance in the banking and financial services sector to ensure ethical, efficient, and effective deployment.
The panel will discuss current applications of AI in the banking and financial services industry, showcasing how these technologies are not just futuristic concepts but present-day tools driving operational efficiency and customer satisfaction. Additionally, the session will cover strategic approaches to overcome challenges and optimize AI investments, setting the stage for a future where AI is a cornerstone of industry innovation and growth.
Understanding AI’s Financial Impact: Gain insights into how Generative AI can significantly enhance productivity and profitability in the banking sector, potentially adding $200 billion to $340 billion annually
AI Governance: Learn about the frameworks and strategies necessary for robust AI governance to ensure ethical, efficient, and effective AI deployment within financial institutions
Real Use Cases of Generative AI: Explore real-world applications of Generative AI in banking and financial services, demonstrating how these technologies are currently enhancing operational efficiency and customer satisfaction
Executive Priorities: Discuss the critical priorities for executives to focus on to maximize the return on investment from AI technologies, including strategic implementation and overcoming common challenges
Navigating AI Integration Complexities: Delve into the complexities of integrating AI technologies in banking environments, from technical challenges to regulatory compliance and beyond
Future Trends in AI and Banking: Look ahead at how Generative AI is expected to evolve and continue transforming the banking industry, potentially leading to new business models and further innovations in customer service and operational efficiency
