BFS Market Modernization and Demand Trends: APAC | LinkedIn Live

November 14, 2024

LinkedIn Live

BFS Market Modernization and Demand Trends: APAC

Watch this event on LinkedIn which was delivered live on Thursday, November 14, 2024

Everest Group experts Pranati Dave, Practice Director, Kriti Gupta, Practice Director, and Suman Upardrasta, Vice President, will explore key modernization initiatives, emerging trends 💹, and the shifting market demands driving transformation across the Asia Pacific banking and financial services (BFS) market .

Join this LinkedIn Live on November 14 as our panel dives into how financial institutions are navigating regulatory changes 🔄, adopting digital technologies, and meeting the needs 🔃of a growing customer base.

Attendees will come away with a solid understanding of the unique challenges and opportunities faced by BFS organizations in APAC as compared to other regions.

During this in-depth LinkedIn Live , we discussed:

What are the key business, technology, and sourcing priorities of different regulations in Asia Pacific ❔?
What is the future operating model ♻️?
What is role of core modernization in driving transformation for BFS enterprises?
What is the role of service and technology providers?

Pranati Dave

Practice Director

Kriti Gupta

Practice Director

Suman Upardrasta

Vice President

Addressing the Doom Loops in Customer Service: An Opportunity of Market Differentiation for Financial Institutions | Blog

September 19, 2024

On August 12^th, 2024, the Biden administration launched a new initiative – Time is Money – to crack down on all the ways that enterprises try to avoid customer queries and issue resolution by trapping them in arduous cycles of automated communication (doom loops), as well as not connecting them directly to a human agent.

Fast forward a few months and the administration has taken an unfavorable view of the situation and since stated that companies have established these cumbersome processes by design, to deter consumers from getting their monetary due (in the form of refunds or subscription cancelation), along with adding to their daily frustration, as inevitably they then profit from customers ultimately giving up.

This blog explores the concept of doom loops and analyses customer pain points and their impact on brand loyalty and regulatory compliance. Additionally, it provides strategic recommendations for enterprises on how to address these issues, particularly in their outsourcing contracts.

Reach out to us to discuss this topic further with our expert analysts.

Introduction: from interactive voice response (IVR) to chatbots

Doom loops refer to the frustrating and often endless cycles customers experience when trying to resolve issues through automated systems. The concept of doom loops in customer service has its roots in the early days of IVR systems, which were widely adopted by companies in the 1980s and 1990s.

IVR systems allowed businesses to handle a large volume of customer calls by automating the initial stages of interaction. However, these systems often became a source of frustration for customers who found themselves trapped in an endless cycle of menu options, unable to reach a human representative or resolve their issues.

As technology advanced, chatbots emerged as a new solution, promising to enhance customer service by providing instant, 24/7 support. However, these chatbots have inherited many of the same issues that plagued IVR systems. Customers often find themselves in a similar doom loop, where the chatbot fails to understand their query, provides inaccurate information, or directs them through a series of irrelevant responses before they can reach a human agent. This problem is particularly pronounced in industry verticals such as banking and financial services where customer inquiries often involve sensitive and intricate issues.

The evolution from IVR to chatbots was intended to improve efficiency and customer satisfaction, but in many cases, it has simply shifted the medium of the doom loop from telephones to digital interfaces. While chatbots offer the potential for greater scalability and personalization, they also present new challenges in ensuring that customer interactions are meaningful and effective.

Consumer pain points and the impact on brand loyalty

Customers’ experiences with chatbots and IVR systems can be frustrating, particularly when they encounter a doom loop. Common pain points include:

Focus on banking and financial services

While doom loops exist across verticals, in the financial services industry, these pain points can have particularly severe consequences. Financial institutions handle sensitive information and transactions, and customers expect a high level of accuracy, security, and responsiveness.

When these expectations are not met, it can lead to a significant decline in customer trust and loyalty. Banks and financial institutions have attempted to address these issues by creating specialized flows for critical areas such as fraud detection, financial crime, and compliance. These flows are designed to quickly escalate issues to human agents, ensuring that high-priority concerns are handled efficiently. However, despite these efforts, many customers still experience frustration, particularly when the automated system fails to recognize the urgency of their issue or mistakenly routes them through a generic flow.

This situation is particularly concerning in the context of fraud detection. Customers who suspect fraudulent activity on their accounts expect immediate and effective assistance. If they are caught in a doom loop, the delay in resolving the issue can lead to significant financial losses and a complete breakdown of trust in the institution.

A prime example of this is the Wells Fargo unauthorized accounts scandal, where sales employees opened millions of unauthorized accounts to meet their targets. Irate customers faced difficulties in account closure and resolving related issues quickly because of long wait times and unhelpful responses, which saw a loss of customer trust, widespread media coverage, frustrated customers and employees, penalties for the organization, and eventually significant customer attrition.

Regulatory scrutiny and potential liabilities

The growing reliance on chatbots and automated systems in customer service has not gone unnoticed by regulators. In recent years, there has been increasing scrutiny from regulatory bodies such as the Consumer Financial Protection Bureau (CFPB) in the United States. These regulators are concerned about the potential for these systems to create barriers to effective customer service, particularly in critical areas such as fraud detection and compliance.

The CFPB, for example, has initiated actions targeting financial institutions that rely heavily on automated systems without providing adequate human support. The agency’s primary concern is that these systems can lead to consumer harm, by delaying the resolution of critical issues or providing inaccurate information. Therefore, it has proposed new rules that would require financial institutions to ensure that customers have easy access to human representatives, possibly by clicking a single button. It is also planning to issue rules or guidance to crack down on ineffective and time-wasting artificial intelligence (AI) or chatbots used by banking and financial services (BFS) enterprises for customer service and identify use cases in which usage of voice recordings (IVR) is illegal.

The implications for financial institutions are significant. Failure to comply with these regulatory expectations can result in substantial fines and legal penalties, not to mention potential damage to the institution’s reputation. In an environment where evolving regulatory compliance is already a significant challenge, the additional burden of ensuring that automated systems do not create doom loops adds another layer of complexity.

Strategic recommendations for brands

Given the risks associated with doom loops in customer service, enterprises must take proactive steps to address these issues. Here are some strategic recommendations:

By taking these steps, enterprises can mitigate the risks associated with doom loops and ensure that their customers receive the level of service they expect and deserve.

Exceptional customer experience = sustained customer trust

The issue of doom loops in customer service is not new, but it has taken on new dimensions in the digital age as brands increasingly rely on automated systems. Ultimately, the success of enterprises in today’s competitive environment depends not only on their ability to manage costs but also on their commitment to providing exceptional customer service.

By focusing on the needs of their customers and avoiding the pitfalls of doom loops, enterprises can build and maintain the customer trust and brand reputation that is essential to their long-term success.

If you found this blog interesting, you can read our Decoding The EU AI Act: What It Means For Financial Services Firms | Blog – Everest Group (everestgrp.com) blog, which delves deeper into the topic of regulatory compliance for financial services firms.

If you’d like to discuss the impacts of doom loops on customer experience in financial institutions in more detail, please reach out to Dheeraj Maken or Aishwarya Barjatya.

Emerging Risk and Compliance (R&C) Outsourcing Needs | Blog

July 1, 2024

In the dynamic landscape of banking, financial services, and insurance (BFSI), risk and compliance (R&C) functions have become critical. Read on to explore the growing trend of outsourcing R&C processes, including the strategic advantages, regulatory considerations, and the role of specialized service providers in bolstering operational efficiency and compliance resilience amid evolving industry dynamics. Reach out to us to discuss further.

Risk and compliance (R&C) functions may not directly generate revenue, but they are crucial for the effective execution of business strategies and ongoing operations of banking, financial services, and insurance (BFSI) enterprises. Conventionally, R&C only receive attention when something goes wrong, like regulatory enforcement. It’s time to adopt a proactive and strategic approach.

Recently, there have been rising volumes for processes related to R&C, putting significant pressure on in-house compliance teams of BFSI enterprises, as the cost of failing to meet R&C mandates is extremely high. For example, Binance faced a US$4.3 billion penalty in 2023 due to lapses in anti-money laundering program. Similarly, in 2024 HSBC has been fined £57.4 million for customer deposit protection failings.

So, what’s the solution? While some BFSI enterprises, due to regulatory requirements or other sensitivities, must keep all compliance activities in-house, for others, outsourcing part or all of their compliance functions is a viable alternative. This shift not only addresses immediate pressures but also positions BFSI enterprises for future resilience and competitiveness.

The catch? Regulatory guidance emphasizes that even when compliance activities are outsourced, the company retains accountability for meeting its regulatory obligations. Hence, the need to have a thorough decisioning strategy when it comes to risk and compliance outsourcing.

Traditionally, R&C outsourcing in the BFSI sector has been limited to areas like KYC, AML, credit risk, operational, and third-party risk management, with some audit support services. However, the industry has recently become more open to outsourcing critical processes such as market and liquidity risk, fraud management and chargeback, enterprise risk management, internal audit support, risk consulting, and ESG services.

Exhibit 1: Risk and compliance value chain as defined by Everest Group

The rising propensity to outsource R&C processes is driven by a multitude of factors, including:

Current macroeconomic headwinds: The ongoing recessionary pressures are putting cost constraints on BFSI enterprises as they navigate a high-interest environment. Outsourcing R&C promises much-needed cost-effectiveness when compared to maintaining an in-house compliance team.

Rising volumes of R&C requirements: Current geopolitical scenarios, such as the Israel-Palestine and Russia-Ukraine conflicts, along with major global elections, have heightened the need for processes like sanction screening and Politically Exposed People (PEP) monitoring. Additionally, the macroeconomic environment, where many are living paycheck-to-paycheck, has led to an increase in fraud and chargeback instances. Outsourcing to specialist firms can help increase efficiencies due to economies of scale and a clear operational focus.

The increasing complexity of R&C processes: Fraudsters have become tech-savvy, and the global regulations keep on evolving. Outsourcing can provide quicker access to advanced systems, such as compliance analytics and AI-based risk models, that might be costly or time-consuming to develop in-house. By outsourcing compliance tasks, BFSI enterprises can focus on their core capabilities and strategic goals, thereby increasing productivity and competitiveness.

Access to specialized talent: As BFSI enterprises expand their compliance efforts and integrate them within core business operations, the demand for skilled compliance talent has risen. Effective compliance management now requires not only financial, legal, and analytical skills but also strong operational experience, a combination that is in short supply and can be complemented by an R&C specialist outsourcing partner.

Evolving enterprise priorities within risk and compliance

The COVID-19 pandemic forced BFSI enterprises to rapidly adapt their operations. As the pandemic evolved into an economic crisis, it triggered unemployment and social unrest, presenting challenges like business disruption, remote work, data security, cyber threats, and increased risk and compliance monitoring.

Failures of major banks such as Silicon Valley Bank, Credit Suisse, Silvergate Bank, and First Republic Bank highlighted the urgent need for continuous investment in legal, risk, audit, and compliance functions amid rising inflation and asset/liability mismatches.

Enhanced regulatory scrutiny is another key factor, as highlighted below:

AI and external data use control: The EU Artificial Intelligence Act, the first comprehensive legal framework for AI, was adopted on March 13, 2024. The new Colorado Division of Insurance regulations require insurers to test AI/data systems for bias
Cybersecurity and data safety: The Consumer Financial Protection Bureau (CFPB) proposed rules on consumer-authorized financial data-sharing, and New York’s expanded cybersecurity rule mandates annual reviews of written policies by a governance committee
Capital and solvency oversight: The Financial Stability Oversight Council (FSOC) finalized a framework for assessing risks to US financial stability, including non-bank financial companies and payment systems. The CFPB proposed supervision of digital wallet and payment apps, while the National Association of Insurance Commissioners (NAIC) seeks to protect consumers by ensuring the solvency of life insurers through revised risk-based capital requirements

This more stringent supervisory environment pressures banking organizations to accelerate remediation efforts and operate with less room for error.

The road ahead

Outsourcing broader R&C is similar to the early days of IT outsourcing, where companies gradually outsourced processes one or two at a time. BFSI enterprises should strategically decide which compliance activities to outsource, ensuring these processes are already stable and effective in-house, as outsourcing alone won’t fix existing issues.

As the R&C landscape evolves, financial institutions must proactively adapt by assigning clear compliance responsibilities, integrating technology (AI, analytics, automation), and establishing robust risk management frameworks. Service providers will be essential in supporting these compliance efforts.

For more on R&C outsourcing trends and achieving regulatory compliance, contact Dheeraj Maken ([email protected]), Kriti Gupta ([email protected]) and Ritwik Rudra ([email protected]), or download our report, “High Tide of Transformation – Financial Crime and Compliance (FCC) State of the Market 2024.”

Don’t miss our webinar, What’s Next in Financial Services? Driving Transformation Through Sourcing, Technology, and Operations, to learn how BFSI firms are driving business transformation in response to the macroeconomic environment, evolving customer needs, the tightening regulatory landscape, and the rapid adoption of AI and cloud technologies.

UK Banks Ramp Up Digital Banking Services and Redefine Operations. What are the Implications for the Outsourcing Industry? | Blog

June 27, 2024

Facing macroeconomic challenges and shifting consumer demands, UK banks are reimagining their operations to stay competitive. This transformation involves cost-cutting, digitalization, and a focus on core business areas. The restructuring opens new opportunities for the outsourcing industry as banks seek third-party support to drive efficiency and innovation. Reach out to us to learn more.

A wave of macroeconomic shifts and evolving consumer demand are driving UK banks to rethink their operating model. The UK financial sector is under pressure amid high inflation, lower interest margins, shrinking profits, and a rise in digital banking services. The top banks of the UK, which have historically concentrated their core business in specific segments such as lending and investment banking, are particularly jolted as the two segments witness a dry business amid the slowdown.

While there were a few banks that began strategic restructuring during the pandemic, the number of banks accelerating transformation efforts has surged in the past two years amid the slowdown. Following are the current key factors that are leading UK banks to reimagine their business:

1. Cost pressure: A competition for deposits has been rising for UK banks as clients shifted to higher-rate products, while new originations have decreased amid a volatile interest-rate environment. Mortgage rates on new loans fell toward the end of 2023 due to a fall in market swap rates. Even as originations recover, lower mortgage rates imply a reduced net interest margin for banks. The cost-to-income ratio increased visibly for key banks in Q4’ 2023 when compared to Q3’ 2023, as highlighted in the exhibit below

2. The need for diversification: A few banks in the UK have begun looking at diversification of their business. Some moved toward restructuring as part of their internal strategic plan, while others, that have their revenue concentration in interest rate-reliant segments took a reactive measure amid a pressured, volatile interest rate environment

1. In 2022, Lloyds announced that it would strive to move away from mortgages to business lines less dependent on interest rates, including wealth management and insurance
2. In the beginning of 2024, Barclays announced its acquisition of Tesco’s retail banking business to further expand its presence in the segment. It was also planning to cut jobs in the investment banking segment
3. In February 2024, Standard Chartered was reported to have been looking at restructuring plans for its investment banking division

3. Evolving customer needs: With the rise in new-age banks such as neobanks, customers in the UK are increasingly switching to these online banks due to their services. By the first half of 2023, neobanks such as Revolut and Monzo were neck and neck with traditional banks such as HSBC when it came to the number of customers in the UK, as highlighted in the exhibit below

As the competition from these banks rises for traditional banks, leading institutions are changing the way they serve their customers. In the past two years, most of the top banks have closed their physical branches due to lesser footfall and a greater move to bring all the services online. A representative list of such branch closures is mentioned below.

Bank	Year of shutdown	Number of branches closed in the UK
Virgin Money	2023	40
Natwest	2024	98
Barclays	2024 & 2025	96
Lloyds Bank (including Halifax and Bank of Scotland)	2024 & 2025	176

How are banks planning to restructure their operations?

Most of the major banks in the UK have begun taking steps to align their internal structure according to market demands. While some banks are focusing on becoming digitally equipped institutions for customers, other banks are undertaking strategic measures to overhaul their business segments. A few of the examples are mentioned below:

Digital banking services transformation:
- In 2022, Lloyds committed to a £1 billion IT spend as part of its digital transformation strategy, with an aim to increase its digitally active customers by more than 10% by 2024
- Santander UK also started its core banking digital banking services journey in 2022. It has migrated its UK commercial customers to a new digital banking platform, Gravity on Google Cloud
Asset sale:
Structural / leadership changes:
- In February 2024, Barclays declared an operational overhaul, including substantial cost cuts, asset sales, and the division of the business into five business segments
- In March 2024, Standard Chartered announced changes to its group management team, as part of which the leadership structure of its major divisions has been overhauled
Switch to private ownership: Natwest is on its way to returning to private ownership, after the UK government announced in May 2024 to cut its stake to less than 23%

What does it mean for the outsourcing industry?

The UK financial industry is finally opening to outsourcing and catching up with global peers. The post-pandemic environment accelerated the digital push but slowed business for institutions. This is driving banks to transform operations through third-party support. Thus, while operations outsourcing slowed in other regions, it grew in the UK by over 10% in FY2023. With many banks still on their way to the restructuring journey, the UK poses a slew of opportunities for the outsourcing industry. Here is our take:

The demand for technology levers such as automation and AI will rise from banks looking to become more digital
Financially distressed banks could look for sale or carveout of their loss-making divisions to revive profits
Banks that have restructured their business divisions may revisit their sourcing strategies. For instance, new business divisions may warrant a new sourcing plan. Meanwhile, the divisions that have come under common leadership may follow similar sourcing strategies, such as having a common vendor at both front- and back-offices

The era of transformation in the UK financial sector has brought about a diverse set of opportunities for outsourcing. In a market that has remained tough to crack in the past, this serves as a good chance for providers looking to make a headway and expand their presence in the region. For questions or to explore this topic further, reach out to Sakshi Maurya at [email protected] or [email protected].

Catch our webinar, What’s Next in Financial Services? Driving Transformation Through Sourcing, Technology, and Operations, to learn about driving business transformation in response to the macroeconomic environment, evolving customer needs, the tightening regulatory landscape, and the rapid adoption of AI and cloud technologies.

Core Banking in the Age of Transformation: A Ride from Legacy to Modernity | Blog

June 20, 2024

For years, core banking systems have been the backbone of financial institutions. But the landscape is shifting, and customers have high expectations. Nimble FinTech startups with cloud-based solutions are challenging traditional banks. In this dynamic environment, core banking systems are under more scrutiny than ever before. Reach out to discuss with us.

Legacy core systems, while reliable, are monolithic and struggle to meet today’s needs for hyper-personalization and real-time experiences. They’re expensive to maintain, slow to adapt, and can’t deliver the seamless, personalized experiences customers now expect. As the volume of transactions increases, the rise of open banking accelerates, and the need for real-time processing picks up, these limitations become clear.

The winds of change: M&A, strategic partnerships, and modernization

The core banking landscape is shifting. Mergers, acquisitions, and partnerships between technology providers and financial institutions are on the rise. This consolidation sends a clear message: modernization is no longer optional, it’s essential for survival. For instance, Visa’s acquisition of Pismo, a cloud-native core banking platform provider. This move strengthens Visa’s ability to offer banks next-generation solutions, while Pismo gains access to Visa’s vast network and expertise.

Banks across various markets are recognizing the need for modernization and are actively partnering with service providers to upgrade their core systems. These collaborations highlight the growing understanding that modernization is key to staying competitive and meeting evolving customer demands.

Progressive banks are adopting next-generation core banking platforms offered by leading technology providers that are:

Cloud-native: Built for scalability and agility in the cloud, enabling banks to adapt quickly
API-driven: Open APIs make it easy to integrate with fintech solutions, fostering a more personalized banking experience
Microservices-based: This modular design allows for faster innovation because components can be swapped out and updated independently

Demystifying modernization: A roadmap for success

Banks are understandably cautious about core modernization due to its critical role in daily operations. Several approaches are available, each with its own pros and cons:

Journey-led progressive modernization: This step-by-step approach prioritizes flexibility by building a digital layer around the core. APIs are exposed for better integration, while legacy parts are gradually replaced with modern microservices. Based on our conversations, this is the most preferred choice (5 out of 10 banks) as it minimizes disruption and allows for incremental changes
Big bang replacement: A complete switchover to a new platform, a faster but riskier approach that requires careful planning and execution. Smaller banks with less complex systems often choose this route (2 out of 10 banks)
Other approaches: Re-platforming, re-factoring, and leveraging a new tech stack for greenfield banking are other options, each suited to specific needs and risk tolerances

However, these approaches are not without their challenges. Change management and the need to decommission legacy systems can be challenging, while progressive change can result in higher costs and the need to adapt to constant technological shifts. Data migration, the availability of a scalable talent pool, vendor lock-in, and cost overruns are additional hurdles that banks must navigate.

Implications and opportunities for service providers

The core banking transformation journey presents a significant opportunity for SPs. Banks will need increased consulting and implementation support as they navigate this complex transition.

The journey-led progressive modernization approach, the most preferred by banks, is a long process that requires extensive guidance. Banks will seek expertise in areas such as modernization and decommissioning strategy, change management, data migration, talent acquisition, and system integration. This translates into a higher demand for consulting services, where providers can leverage their industry knowledge and technical expertise to guide banks through the transformation journey.

The road ahead: A collaborative future

The future of core banking is a collaborative one. Banks and SPs will need to work together to unlock the full potential of next-generation core banking solutions. By embracing innovation and forging strategic partnerships, banks can stay competitive and deliver the exceptional experiences that customers demand. This transformation goes beyond just a modernized core; it paves the way for a future of hyper-personalized financial experiences.

Currently, technology providers can participate in our Core Banking Technology Top 50™ Report assessment. We will rank technology providers based on their scale of core banking business, client geography mix, and significance within the core banking platforms market (retail and commercial). Submit a request to participate.

To learn more about core banking, contact Ronak Doshi, [email protected], Pranati Dave, [email protected], Kriti Gupta, [email protected], and Laqshay Gupta, [email protected].

Sourcing BFSI leaders can also request to join the exclusive virtual roundtable, Banking, Financial Services, and Insurance Leaders Discuss: 2024’s Top Trends in Tech and Ops Sourcing, to learn about the latest trending issues shaping tech and ops sourcing within the BFSI sector.

Navigating the Landscape: The Cost and Benefits of Generative AI Implementation | Blog

April 3, 2024

Generative AI (gen AI) can significantly benefit the BFSI industry. However, it can be an expensive investment, making it critical for enterprises to conduct a cost-benefit analysis before implementation. Explore the various costs and advantages associated with this technology in this blog, or get in touch to find out more.

Gen AI has recently gained considerable attention in the banking, financial services, and insurance (BFSI) industry. Many use cases that go beyond creating or summarizing content are being explored throughout the value chain.

Implementing gen AI can improve the velocity of change, increasing the overall efficiency of existing tasks. This technology can streamline operational processes, automate tasks, and enhance customer experience by fostering engagement through tailored experiences. Moreover, it can potentially drive innovation to create change or transformation by generating unexplored ideas, optimizing products, and identifying new market opportunities. Ultimately, this positions enterprises for continuous evolution and success.

BFSI enterprises have recognized the transformative potential of adopting gen AI, which undoubtedly can disrupt existing enterprise models. In the race to get the early advantage, enterprises face challenges as they reallocate funds from other projects and seek to secure new investments to finance new AI and gen AI initiatives.

Concurrently, cloud costs emerge as a significant concern that can potentially escalate when training AI models. However, the overall cloud cost impact from gen AI hinges on specific use cases and model architecture.

A cost-benefit analysis becomes imperative as gen AI-driven use cases are limited, and most can be explored through other AI technologies. This is particularly important because other relatively less expensive technologies can achieve comparable outcomes with similar efficiency.

While gen AI has generated a lot of hype and rapid investment, it’s not currently viable to implement the technology almost everywhere without understanding the cost implications for achieving the potential gen AI benefits. Let’s explore this further.

Exploring Cost and Generative AI Benefits

Below are some of the high-cost categories across the value chain to consider:

Infrastructure and compute

The computational backbone, encompassing graphic processing units (GPUs), tensor processing units (TPUs), and energy consumption, constitutes a substantial investment. Building and maintaining a powerful infrastructure is pivotal for running complex algorithms and training sophisticated models.

Model training or fine-tuning

Gen AI implementation comes with many fixed and variable costs. Training or fine-tuning gen AI models to meet specific requirements is intricate, involving significant computational resources, expert oversight, and time. These costs are substantial but also the foundation for the gen AI model’s efficacy and adaptability.

Data acquisition, preparation, and processing

Performance is heavily influenced by the data quality on which these models are trained. Collecting, cleaning, and storing data can come with high costs to acquire, prepare, and process diverse and high-quality datasets. Ensuring diverse and representative datasets while maintaining data quality standards can be challenging, ultimately impacting the accuracy and reliability of gen AI outputs. At the same time, acquiring high-quality data for training gen AI models and holistic data readiness initiatives can be expensive and require significant capital investments, especially if specialized or proprietary datasets are required.

Security measures

In a highly regulated industry like BFSI, where data and security are imperative, meticulous attention to security and regulatory compliance is critical. Implementing robust security measures cannot be compromised.

However, this adds costs for deploying cybersecurity measures, encryption protocols, and access controls to protect sensitive financial data, notwithstanding increased investments in security technologies, routine audits, and adherence to industry standards.

Considering that gen AI often relies on large datasets, managing personally identifiable information (PII) necessitates strict adherence to data privacy regulations.

Privacy-preserving techniques, anonymization processes, and implementing consent management systems to meet compliance requirements can be costly. On top of that, continuous monitoring and regular audits are essential to maintain compliance and security standards, contributing to ongoing operational expenses.

Integration and service

Not all models run independently and often require integration with existing systems. Seamlessly integrating gen AI into existing workflows and providing continuous support have financial implications. The processes of customization, compatibility checks, and uninterrupted service provision collectively contribute to the overall expenditure.

Regulatory compliance

Operating within a highly regulated BFSI industry with standards such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and industry-specific regulations necessitates additional investments in compliance monitoring, data governance, and legal counsel.

Non-compliance with these regulations may lead to fines and legal consequences. As the regulatory laws for gen AI are still evolving, enterprises must be vigilant.

In light of the dynamic regulatory landscape, remaining flexible to accommodate incoming regulations is crucial.

Post-implementation

Following deployment, continuous monitoring and proactive maintenance of the systems are demanded to ensure gen AI’s sustained performance. Although this is an ongoing expense, these measures are pivotal for adaptability and longevity.

Talent related costs

Enterprises may incur expenses related to recruitment efforts, training programs, certification courses, and retention strategies to attract and retain top talent in the competitive gen AI landscape. As gen AI continues to evolve and play a pivotal role in digital transformation initiatives, businesses must carefully consider and budget for talent costs to ensure successful implementation and utilization of advanced AI technologies.

While investments in gen AI and related technologies are crucial, enterprises must also invest in their human capital by empowering employees with the skills and knowledge needed to thrive in today’s digital age. Effective leadership and a commitment to upskilling and reskilling will drive successful technology adoption and foster an organizational culture of innovation and agility.

The outlook for cost reduction efforts

While gen AI comes with a high cost, the landscape is evolving daily. Technology companies are substantially investing in developing proprietary AI chips and more efficient architectures, a strategic shift that aims to diminish reliance on expensive alternatives.

Enterprises can also explore a micro use case-led approach to implementing gen AI, deploying small, focused areas where gen AI can deliver clear and measurable benefits. Targeting smaller tasks allows for quicker development and deployment of gen AI solutions, leading to faster ROI (Return on Investment). Micro use cases provide opportunities to test and learn from gen AI implementations, enabling continuous improvement and informing future deployments. Smaller projects require less time and resources compared to developing a large, complex gen AI system.

Moreover, the gen AI domain is experiencing a notable training cost reduction, with some solutions claiming a remarkable 50% reduction. These advancements signal a significant stride toward enhancing AI’s capability and affordability, marking a pivotal turning point in the technology’s ongoing evolution.

While the costs associated with gen AI implementation are evident, the benefits in specific uses can significantly outweigh the expenses. Balancing financial considerations and the innovation potential is key. Enterprises must align their AI strategy with business objectives to position themselves at the forefront of innovation and competitiveness.

To discuss gen AI in BFSI, please reach out to [email protected], [email protected], and [email protected].

Looking for use cases for gen AI? Check out our LinkedIn Live on Distinguishing Gen AI Hype from Real Application, or read our latest research on generative AI and its adoption potential.

Unlocking Enterprise Preparedness for T+1 Settlement: The Crucial Role of IT and Technology Services Providers | Blog

March 19, 2024

By partnering with IT and technology services providers, banks and financial institutions can prepare for the new T+1 settlement. This security trade rule change to shorten the order finalization date by a day is expected to enhance operational efficiencies and reduce risk. Read on to understand how this updated regulation will impact the industry landscape and rapidly transform critical areas. Reach out to discuss the topic with us.

In today’s ever-evolving financial industry, the shift to T+1 settlement aims to enhance market efficiency, reduce counterparty risk, and align North American markets with global standards.

The transition to a T+1 settlement cycle represents a monumental shift for banks and financial institutions that will impact trade management, resource allocation, and risk mitigation.

Scheduled to go into effect this May in the US and Canada, the amended rule would require trades to be settled just one day after the transaction date, marking a significant departure from the current two-day cycle.

Let’s explore its ramifications further.

Impact on investors

Shifting toward instantaneous or faster settlements is a remarkable milestone that will streamline operations, improve risk management, boost liquidity, and provide better counterparty risk management. This will further lead broker-dealers to reduce margins and collateral requirements.

Accelerating settlement cycles will save buyers and sellers time and increase trading volume. The positive impact will vary by the investor type. Large institutional investors like corporations will benefit from more liquidity and reduced margin requirements. Meanwhile, small or retail investors, who contribute significantly to the daily exchange trading volumes, will receive funds or assets post-execution faster. This will bring various operational benefits and improve market risk mitigation.

However, the proposed shift will require significant investor education and resilience to overcome the negative market sentiment caused by affected broker businesses. Investors will grapple with the shorter period for trade settlement, and brokers will need substantial investments to update front-to-back-office systems. Moreover, the higher settlement costs could potentially disappoint investors.

Implications for banks and financial services institutions

The shift to T+1 settlement presents a significant opportunity for the financial industry to enhance operational efficiencies and reduce risk. As Martin Palivec, Head of Securities Services, Canada, Citi, has highlighted, many post-trade processes still rely heavily on manual intervention, indicating a clear need for automation and streamlining. Settlement compression will drive the industry to strengthen and automate these processes, leading to more efficient and reliable operations.

Moreover, improving reporting capabilities is becoming increasingly critical, with clients expecting timeliness and accuracy in trade status updates. This underscores the urgency for organizations to adopt advanced technologies and modernize their post-trade infrastructure.

Role of IT and technology service providers

By partnering with IT and technology services providers, banks and financial institutions can navigate the complexities of T+1 settlement and position themselves for success in the evolving financial landscape.

IT and technology services providers can play a crucial role in assisting banks and financial services institutions in their T+1 journey in the following ways:

System Upgrades and Integration: Technology providers can help firms upgrade their existing systems or integrate new systems to support T+1 settlement. This includes implementing real-time processing capabilities, enhancing data management systems, and ensuring interoperability with other market participants
Automation and Straight-Through Processing: Automation is key to achieving operational efficiency in a T+1 settlement environment. Technology providers can assist firms in automating trade interfaces, matching and affirming trades in real time, and streamlining settlement workflows to minimize the risk of failed settlements
Data Management and Reporting: Accurate and timely data management is critical with the compressed settlement timeline. Technology providers can help firms improve data quality, implement real-time reporting capabilities, and enhance communication with counterparties to reduce the risk of errors and delays
Regulatory Compliance and Risk Management: Technology providers can assist firms in ensuring compliance with T+1-related regulatory requirements, including implementing systems to monitor and report trade status and manage risk factors associated with the new settlement cycle

The move to the T+1 settlement represents a significant advancement in the financial industry, necessitating operational and technological adjustments for banks and financial institutions.

As this transition unfolds, the prospect of T+0 settlement looms, with countries like India already making strides in this direction. The adoption of distributed ledger technology (DLT) is expected to be pivotal in enabling T+0 settlement and offering real-time, secure, and transparent transaction processing.

Organizations preparing for T+1 should also consider the potential shift to T+0 in their strategic planning. By embracing technologies and practices supporting T+1 and T+0 settlement, businesses can streamline operations, enhance efficiency, and stay ahead of the curve in an evolving financial landscape.

To learn more about the impact of T+1 settlements in the financial services industry, contact Abhinav Rathaur, [email protected], Kriti Seth, [email protected], and Pranati Dave, [email protected].

Read the blog, Beyond the Hype: Approaching Gen AI in BFSI Enterprises with the Generative AI-EXCEL Framework, to learn about successful gen AI adoption in the BFSI sector.

Beyond the Hype: Approaching Gen AI in BFSI Enterprises with the Generative AI-EXCEL Framework | Blog

March 19, 2024

To successfully adopt Gen AI in BFSI, enterprises need to consider four fundamental aspects that can lead to responsible and effective deployment. Carefully evaluating each framework component is essential to ensure a positive Gen AI journey. Read on to learn about the Generative AI-EXCEL Framework and the importance of each element, or get in touch.

As there is urgency to embrace Generative Artificial Intelligence (Gen AI) across all industries – the BFSI industry is no exception given its prevalence. However, a thoughtful approach is required to fully reap the benefits of Gen AI.

Before immersing themselves in various use cases and integrating Gen AI into their operating structure, BFSI enterprises should strategically examine four fundamental components along the Gen AI value chain:

Generative AI-EXCEL framework

Enable AI
Execute AI
Champion AI Operations
Lead AI Change Management and Governance

These elements can guide enterprises toward harnessing the full potential of Gen AI in BFSI while ensuring responsible and effective deployment.

Enable AI

Embarking on AI initiatives demands the expertise of AI experts to define a clear vision and strategy. Seeking guidance from Gen AI experts is essential in laying a solid foundation for successful implementation. Assessing organizational readiness through an AI maturity and readiness assessment is recommended as this can provide insights into preparedness levels and potential challenges.

Developing a Gen AI roadmap and conducting a Return on Investment (ROI) analysis further ensures a well-structured approach, allowing organizations to navigate the complexities of integrating Gen AI effectively in their operations. A thoughtful approach is essential for consulting and enabling generative AI across the value chain before delving into specific use cases, relying on AI technology partners, and tool selection advisory services to ensure that organizations secure the right resources for success.

Adequate resources are crucial to ensure scalability, allowing Gen AI systems to manage increasing workloads efficiently. There is a lot of demand for talent, skills, and domain expertise, especially in Gen AI that needs to be plugged.

Moreover, hardware and infrastructure compatibility and version compatibility among different Gen AI models and frameworks are essential for seamless operations. Massive datasets play a pivotal role in training large-scale AI models, demanding significant computational power from specialized hardware such as graphics processing units (GPUs) and tensor processing units (TPUs). Balancing these elements is vital to harness the potential of Generative AI effectively.

Execute AI

When developing AI systems, some essential steps include preparing the data, refining features, utilizing and fine-tuning pre-built models, integrating AI with existing systems, creating custom models as needed, and conducting thorough testing to ensure reliability.

The increasing complexity of Gen AI models has led to the emergence of Machine Learning Operations (MLOps) and Large Language Model Operations (LLMOps) as services. These can play a pivotal role in easing the efficient deployment, orchestration, and monitoring of AI models.

Given the possibility of potential biases introduced by Gen AI, it becomes imperative for BFSI enterprises to ensure fairness. Vigilant model monitoring and drift analysis are some ways to achieve this. In addition, optimized performance can be achieved by incorporating accelerators.

Champion AI Operations

A robust change management strategy is essential for navigating a smooth transition. Leadership communication about AI’s benefits can set a positive tone for adoption. Equipping workforce with the necessary skills through comprehensive training and upskilling is essential. Developing a streamlined process for Gen AI adoption can enhance its acceptance rate. Recognizing and reinforcing Gen AI’s contributions can motivate the workforce, ensuring effective and sustainable AI integration.

Lead AI Change Management and Governance

Strong data governance can help address some of the concerns related to source attribution and confidence levels in data and foster trust in Gen AI outcomes.

Gen AI can generate content that is low in authenticity. Model explainability can help make AI decisions more understandable and traceable, boosting user confidence. Furthermore, enforcing compliance, validation, and auditing mechanisms can reinforce AI solutions’ reliability and ethical deployment.

The Gen AI model can potentially produce biased or dangerous results. Other AI models can be used to test results for risky outputs. Enterprises can also use data loss prevention and other security tools to prevent users from inputting sensitive data into prompts in the first place. Maintaining control over data is essential, and multiple levels of security are required.

In an industry where data security and privacy are paramount, governance becomes a linchpin for safeguarding sensitive information. Beyond regulatory compliance, governance can address critical aspects such as risk management, fairness, transparency, and accountability. With ongoing regulatory uncertainty and evolving laws, it is critically important to exercise caution about data breaches, privacy violations, or biased or discriminatory decisions that can create regulatory liabilities.

By following this Generative AI-EXCEL framework, BFSI enterprises can ensure they have addressed all essential aspects of enabling Gen AI. From identifying the right infrastructure and resources to developing and testing models and ensuring proper change management and governance, thoroughly evaluating each component guarantees a smooth AI transition. This approach will allow BFSI enterprises to harness Gen AI’s power fully.

To discuss Gen AI in BFSI, please reach out to [email protected], [email protected], and [email protected]. Learn more about how we can help your enterprise to leverage Gen AI, or read our report on revolutionizing BFSI workflows with Gen AI.

Generative AI and Cloud Integration Keep Mainframes Alive in the BFSI Industry | Blog

March 13, 2024

Though a recent Everest Group survey revealed the pressing need for mainframe modernization, the technology is far from dead in the banking, financial services, and insurance (BFSI) industry. The rise of generative AI (gen AI) will encourage more BFSI firms to adopt a comprehensive technical architecture, integrating cloud and mainframe technology at its core. Read on for insights from the survey or get in touch.

Are BFSI firms really ditching mainframes? The BFSI industry is indeed grappling with the prospect of abandoning this approach. According to an Everest Group survey, about half of the respondent firms have shifted their peripheral tasks away from mainframes.

Concerns about mainframe system scalability loom large for more than 50% of sizable BFSI firms, while about 60% of smaller firms struggle primarily with finding talent skilled in older programming languages such as COBOL.

Operational complexity with mainframe systems is also reported as a challenge by over 90% of BFSI respondents, underscoring the pressing need for mainframe modernization. Evolving priorities such as building data-driven workflows, digitalization, and enhancing customer experiences further fuel this urgency.

Cost efficiency and talent unavailability are the main drivers for mainframe modernization, closely followed by the imperative for innovation. North American firms prioritize core banking and CRM workloads over modernization, while European players emphasize digital channels and payment infrastructure.

Despite these challenges, mainframes are expected to remain integral to BFSI operations. A significant majority, about 60% of the respondent firms, have not yet started modernizing their core systems. In the coming years, non-core applications will continue to have a higher migration rate than core applications.

However, industry research underscores that BFSI enterprises optimize and enhance their mainframe ecosystems, presenting a promising opportunity for service providers to assist. Let’s explore this further.

Capturing cloud value through a hybrid infrastructure

With mainframes here to stay in the BFSI industry, enterprises can gain a competitive advantage by investing in the private cloud to capture the underserved and large demand for hybrid IT. Hybrid cloud is a constant across all our BFSI industry enterprise conversations.

Of the respondent BFSI firms, 35% utilize private cloud for their modernization initiatives — mainly in a hybrid cloud environment — while 65% rely on a multi-cloud strategy.

IBM’s focus on transforming the mainframe’s interface to other environments validates this trend. The launch of z15 and z16 is the company’s answer to the age of cloud computing. It is an evolution to meet the needs of hybrid cloud deployments, leveraging investment in data, generative Artificial Intelligence (gen AI), and applications, adding features and functionality to complement this strategy. IBM is focusing its messaging on rightsizing over downsizing. The strategy to provide more flexibility, predictability, and cost-effectiveness is evident in the company’s push for tailored fit pricing.

The survey reveals many firms believe the disconnection between mainframe environments and new cloud-native systems and applications is a big challenge. Further investments in technologies like application programming interfaces (APIs), in collaboration with technology and service providers, will help bridge this gap in the coming years.

Will banking’s AI revolution enable cloud-based modernization?

We expect a symbiotic relationship between gen AI and IT modernization, each complementing the other’s growth. Cloud computing is the foundational block providing the right computational power to run AI applications, while AI’s enhanced speed and efficiency will support cloud migrations.

BFSI firms are channeling investments into gen AI, crafting use cases to support their modernization initiatives and business operations. The survey found that 40% of the BFSI firms have proof of concepts or use cases for gen AI to support mainframe modernization.

Firms have moved beyond experimenting with gen AI. Goldman Sachs and Deutsch Bank have started using gen AI to generate code and refactor their modernization initiatives, closely watching the impact. They are building and rolling out use cases to improve operational efficiency. We believe that the banks poised for future success are identifying use cases that solve specific business problems aligned with their organization’s strategy. This can enable them to measure the results easily and encourage leadership buy-in.

With mainframe modernization services growing at a steady 4-5%, the ability to adapt and innovate using newer technologies such as gen AI will drive more BFSI firms to adopt a more robust and holistic technical architecture with both cloud and mainframes at its core.

However, the question remains: will gen AI bring exponential change in the next three years? There is one certainty: the need for a strong IBM, service provider, and hyperscaler value proposition will continue to grow for BFSI clients.

To discuss mainframe modernization further, please contact [email protected] and [email protected]. Understand more about the future of the enterprise mainframe, or watch our on-demand webinar on the future of generative AI implementation at enterprise level.

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