The challenge of text summarization in financial services

The financial services industry generates an immense volume of documentation daily. From customer interactions and regulatory filings to legal proceedings and risk assessments, organizations must process, interpret, and act upon large amounts of unstructured data. Traditionally, this has been a time-consuming and labor-intensive process, often susceptible to human error and inconsistencies. As regulatory frameworks evolve and customer expectations rise, the demand for accurate, efficient, and standardized document summarization has never been more critical.

In banking, institutions must navigate a constantly shifting regulatory landscape. Compliance teams are responsible for reviewing extensive regulatory filings, risk reports, and audit documents—any misinterpretation can result in significant financial and legal consequences. Beyond compliance, customer service operations require rapid access to key insights from call center interactions to enhance service efficiency. Additionally, loan and credit risk assessment teams manually analyze financial statements, credit histories, and other documents to determine creditworthiness, a process that is both time-intensive and costly.

The insurance sector faces similar challenges, particularly in underwriting, policy management, and claims processing. Insurance providers must constantly interpret complex regulatory changes while ensuring accurate policy underwriting and risk assessment. Claims processing teams review medical reports, legal documents, and third-party assessments to determine coverage and fraud risk. Manual document reviews in these areas not only slow down operations but also introduce inconsistencies that can impact decision-making.

The increasing complexity of financial services documentation makes manual summarization an unsustainable approach. Generative AI (GenAI) offers a powerful solution by enabling automated summarization of key insights from various documents. However, assessing the quality of AI-generated summaries remains a challenge. Traditional evaluation methods, such as ROUGE and BERTScore, rely on human-generated references, which are not always available or practical for large-scale financial services applications.

Introducing QAG-based automated summarization evaluation

Question-Answer Generation (QAG) for automated summarization evaluation provides a breakthrough, offering a reference-free approach to ensuring both completeness and accuracy in AI-generated summaries. Instead of comparing summaries to predefined references, QAG-based evaluation gauges summarization quality by generating factual questions from the original document and checking whether the AI-generated summary provides correct answers.

Experimental results

Optimization techniques for QAG were implemented that included limiting truth extraction and using custom question templates to improve evaluation performance.

This enhanced QAG-based evaluation approach was then tested on four real-world transcripts. In each test, both the default QAG model and our optimized approach were implemented. The following table summarizes the results:

Overall, the experimental results reveal a significant leap in alignment scores, rising from a baseline of 56% to over 70%, while coverage scores experienced an even greater boost, increasing from 70% to 90%. These enhancements demonstrate the effectiveness of the refined approach in producing more accurate and comprehensive AI-generated summaries.

Wide-ranging use cases in banking and insurance

By implementing QAG-based evaluation, financial institutions can improve the reliability and accuracy of GenAI-powered summarization across multiple business functions. In banking, it ensures that compliance reports, customer interactions, and financial risk assessments maintain factual integrity. In insurance, it enhances underwriting decisions, policy management, and claim evaluations. The following is a sample of several key use cases in financial services.

Banking use cases

• Call center interaction summarization: Customer service teams manage a high volume of customer interactions, often recorded in call center transcripts, chat logs, and emails. GenAI can summarize these conversations, extracting key themes, customer concerns, and sentiment trends, enabling more efficient issue resolution. With QAG-based evaluation, AI-generated summaries ensure that no critical customer concerns are overlooked, allowing for more personalized and proactive customer support.

• Audit report summarization: Internal audits are a critical part of risk management in banking, yet the process is often time-consuming and labor-intensive. AI-powered summarization helps highlight key discrepancies, compliance violations, and recommended actions from audit reports, improving the efficiency of risk and compliance teams. With QAG-based evaluation, banks can ensure that summarized audit findings remain aligned with the original reports, reducing the chances of oversight in risk assessments.

• Credit risk assessment: Evaluating a borrower’s financial health requires the review of credit reports, financial statements, and loan histories, often spread across multiple documents. GenAI can consolidate key financial indicators into a structured summary, allowing risk analysts to make faster and more informed lending decisions. By applying QAG-based evaluation, banks can verify that these summaries accurately reflect the borrower’s financial status, reducing errors in credit risk assessments.

Insurance use cases

• Underwriting and risk assessment: Insurance underwriting requires the evaluation of extensive data, including health records, financial documents, and previous policy claims. GenAI-generated summaries allow underwriters to quickly assess risk factors, policy eligibility, and pricing considerations. With QAG-based evaluation, insurers can confirm that these summaries capture the full scope of risk assessment criteria, reducing underwriting errors and improving decision-making efficiency.

• Policy management: Managing policies involves handling a large amount of unstructured documentation throughout the policy lifecycle. Any modifications initiated by insurers or customers require careful reassessment. GenAI streamlines this process by efficiently condensing information from various sources. By applying QAG-based evaluation, insurers can confirm that AI-generated summaries align with policy terms and regulatory requirements, enabling them to allocate more time to strategic tasks such as customer service and relationship management.

• Claims processing: Whether for auto, healthcare, or commercial policies, claims processing is a complex, documentation-heavy task that demands significant time and effort when done manually. GenAI automates the extraction of critical details from diverse records. QAG-based evaluation ensures that all necessary claim details are preserved, reducing operational costs, expediting claim settlements, and improving overall customer satisfaction.

These use cases highlight just a few of the many ways QAG-based evaluation can be applied in financial services. Potential applications extend far beyond these examples. Depending on an organization’s specific needs, QAG-based evaluation can be adapted to review AI-generated summaries across a wide range of business functions, including regulatory reporting, contract analysis, investment research, internal policy compliance, and more.

Driving accuracy, efficiency, and trust in AI-generated summarization

As financial institutions increasingly rely on GenAI to streamline document processing, ensuring the accuracy and reliability of AI-generated summaries is paramount. QAG-based automated summarization evaluation provides a reference-free, scalable, and precise method to assess summarization quality, addressing one of the key challenges in AI adoption. By evaluating summaries based on factual correctness and content coverage, QAG-based evaluation offers a structured approach to verifying AI outputs without the need for human-generated reference summaries.

The benefits of integrating this approach in banking and insurance are far-reaching. Banks can enhance decision-making by quickly extracting key insights from financial reports, compliance documents, and customer interactions. This leads to faster responses to regulatory changes, improved operational efficiency, and a more seamless customer experience. In the insurance sector, QAG-based evaluation improves underwriting accuracy and claims processing efficiency, ensuring that AI-generated summaries are both comprehensive and aligned with business objectives.  

Now is the time for financial institutions to embrace AI-powered summarization with QAG-based evaluation. To explore how this approach can elevate your organization’s AI-driven summarization efforts, contact Capgemini’s Financial Services Insights & Data team today.  

Generic proposals are losing deals

Insurance RFP responses are starting to feel like they’ve been photocopied over and over. Brokers and clients today are no longer just flipping through proposals hoping to find a winner—they’re expecting them to speak directly to their unique needs. The days when you could get away with templated, one-size-fits-all responses are behind us. In insurance, trust is built on understanding, and understanding is signaled through specificity.

In fact, many proposals don’t even get past the first skim because they sound like they were written for any client, not this client. The root issue is that generic responses signal a lack of investment in the relationship. Insurers risk losing out on high-value deals, wasting time and resources crafting responses that don’t convert. As our work with numerous global insurers has revealed, many of these generic documents—especially cover letters and executive summaries—were not even being read by brokers due to their lack of relevance.

Generative AI for hyper-personalization in insurance

Now, let’s imagine a private, enterprise-trained generative AI assistant that doesn’t just regurgitate past language, but crafts messages so tailored they make your clients feel like VIPs. That’s the magic of a custom, private GenAI assistant.

This assistant is no off-the-shelf chatbot. It’s trained on your historical RFP data, your previous client interactions, your industry nuances, and even your internal product literature. It understands how you communicate and what your clients care about. More importantly, it learns and evolves. With the help of Agentic AI, a modular framework powered by specialized AI agents, this assistant goes far past simple auto-fill. It reads the RFP, summarizes the client ask, constructs the top winning themes, and proactively drafts personalized responses, summaries, and even intelligent suggestions for improvement.

This is where hyper-personalization becomes real. By utilizing structured and unstructured data alike, the Gen AI assistant pulls out the most relevant insights and shapes them into messaging that resonates. It compiles data from its entire knowledgebase to craft a tailored solution to the client’s problem. It’s not guessing, it’s contextualizing. That means proposals land stronger, faster, and with far better chances of hitting the mark.

MongoDB: The motor powering AI-driven personalization

Behind the scenes, MongoDB plays a crucial role in making all this magic possible.

Their flexible document model allows for rapid ingestion of diverse data types including past RFPs, client correspondence, marketing decks, and everything else imaginable. This structure is perfect for insurers juggling massive volumes of semi-structured and unstructured data.

MongoDB Atlas Vector Search is particularly crucial here.  It enables the Gen AI assistant to rapidly identify, rank, and re-rank the most relevant information based on contextual relevance, delivering responses that are both timely and precise.

Its globally distributed architecture—available across AWS, Azure, and GCP in over 115+ regions—makes it an ideal foundation for building large-scale, enterprise-grade Gen AI applications. By embedding Vector Search directly into the core database, MongoDB eliminates the need to sync data between separate operational and vector databases. This simplification reduces complexity, minimizes the risk of errors, and significantly shortens response times.

Keeping both operational and vector data in a single system also improves performance through reduced latency and advanced indexing capabilities. For organizations building out agentic Gen AI capabilities, MongoDB further supports Graph RAG (Retrieval Augmented Generation) architectures, enhancing contextual accuracy and scalability across use cases.

However, insurance is a heavily regulated industry and data security is critical. MongoDB also offers enterprise-grade encryption, access controls, and supports compliance with key data privacy regulations.

Case study: Less robotic, more calibrated and compelling RFPs at a global insurer

A recent standout example of our custom, private GenAI assistant in action comes from a global insurer who started with a modest request: Can we hyper-personalize our RFP cover letters better? The ask was simple and they were merely looking for a few bullet points to make things feel less robotic.

What we were able to create for them was a revolution in how they respond to RFPs. In just five weeks, our team implemented our custom, private GenAI assistant that not only delivered personalized bullet points but also crafted full executive summaries and tailored cover letters. These were not piecemeal templates—they were coherent, compelling, and calibrated to the specific opportunity at hand.

The feedback we received was immediate and enthusiastic. The Chief Innovation Officer and the Sales leadership team pushed for scaling the solution to other areas. It wasn’t just a productivity gain, it was a reputation builder. Brokers began to take notice. The insurer wasn’t just responding faster; they were responding smarter.

Business impact, check! Strategic outcomes, check!

By implementing a custom, private GenAI assistant, insurers gain access to a scalable, cloud-native platform that integrates easily with existing systems—whether it’s a CRM, document management platform, or internal knowledge base. Beyond the technical flexibility, the real impact lies in how this approach transforms stagnant, siloed data into living insights that power tailored client engagement.

The platform supports more consistent and efficient proposal development by reducing manual effort, accelerating turnaround times, and improving the quality and relevance of responses. Teams can focus less on reformatting and more on building client relationships. Meanwhile, the built-in security and governance measures ensure that every interaction meets enterprise compliance standards, protecting both client data and institutional knowledge.

Insurers using this model report stronger broker engagement, better win rates, and faster RFP response times. Operational costs drop due to reduced manual formatting and response drafting. From a technical perspective, RAG-enhanced GenAI can offload up to 35% of compute cost compared to full LLM inference on raw content, thanks to targeted document retrieval and short-form reasoning tasks.

As organizations use this solution over time, feedback loops from won/lost deals can be fed back into the model for retraining, improving response quality and alignment. As the assistant matures, it can serve as a strategic enabler across adjacent workflows—claims review, renewal briefs, or even sales coaching.

The future of insurance RFPs

Custom private GenAI assistants represent a rare intersection of technical maturity and business impact. When combined with MongoDB’s robust data orchestration capabilities and Capgemini’s proven technology blueprint, this solution becomes more than a digital enhancement—it becomes a strategic advantage.

Organizations that embrace this model transition from reactive, templated proposal development to proactive, context-rich client engagement. With the ability to generate intelligent, personalized content at scale, they not only improve operational efficiency but also strengthen their competitive position in a high-stakes market.

This isn’t just about responding faster—it’s about responding better. As expectations around relevance, precision, and value continue to rise, the future of insurance RFPs will belong to those who invest in intelligent automation and meaningful personalization.

The path forward isn’t generic. It’s personal, scalable, and ready to deliver lasting impact.

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The struggle to meet rising customer expectations

Customer expectations for financial services firms have never been higher. Whether submitting a mortgage application to a bank or contacting an insurer to file a claim, consumers demand seamless, hyper-personalized, and efficient service at every interaction. However, many financial services contact centers still rely on outdated models that struggle to meet these expectations. Long wait times, fragmented communication channels, and manual processes create frustrating experiences for both customers and agents.

The disconnect between what customers expect and what traditional contact centers deliver is becoming increasingly untenable. According to Capgemini’s World Retail Banking Report 2025, only 24% of customers are satisfied with their bank’s contact center interactions. Customers cite long wait times (61%), inconsistent communication across channels (65%), and gaps in real-time updates between digital and in-person interactions (63%) as major sources of frustration​. These pain points are equally prevalent in the insurance sector, where policyholders frequently encounter delays when filing claims, updating policy details, or seeking assistance during critical life events.

Operationally, these challenges extend beyond customer experience. Many financial services firms continue to operate contact centers where over 80% of an agent’s workday is consumed by repetitive, manual tasks, leaving little room for value-driven customer engagements​. For insurers, this often means agents spend excessive time manually verifying policyholder information, processing claims, or handling routine inquiries—tasks that could be streamlined through automation. Similarly, in banking, less than 10% of agent time is spent on revenue-generating activities such as up-selling and cross-selling, leading to missed opportunities for business growth​.

The demand for change has never been more pressing. Financial institutions must move beyond incremental improvements and embrace AI and generative AI (GenAI)-powered contact centers that blend automation, real-time analytics, and live agent support. By integrating these capabilities, banks and insurers can significantly reduce operational costs, improve customer satisfaction, enhance compliance monitoring, and strengthen fraud detection efforts. Those that adopt AI and GenAI-based solutions will be well-positioned to turn their contact centers from cost-heavy service departments into strategic hubs of customer engagement, operational efficiency, and revenue generation.

AI-powered contact centers: The key to efficiency and growth

By integrating advanced AI and GenAI technologies, banks and insurers can create smarter, more responsive, and cost-effective contact centers. From real-time insights that improve self-service interactions to automated workflows that streamline post-call processes, these tools are redefining efficiency and service quality.

Improving efficiency through real-time speech recognition

Efficiency is at the heart of a well-functioning contact center, yet traditional workflows burden agents with manual notetaking, post-call documentation, and slow information retrieval—all of which extend call durations and reduce productivity. AI-powered real-time speech recognition technology is improving agent workflows by providing instantaneous transcription, automated notetaking, and intelligent response suggestions, allowing agents to focus on engaging with customers rather than administrative tasks.

NVIDIA® Riva, a collection of GPU-accelerated multilingual speech and translation microservices, enables firms to create or fine-tune open-source automatic speech recognition (ASR) models to better comprehend sector, function, and firm nuances to generate highly accurate transcriptions at low cost. By leveraging NVIDIA® NIM™ and NIM™ Operator for scalability, financial institutions can seamlessly deploy instant transcription across large-scale contact centers without disrupting existing workflows.

Bolstering customer satisfaction through deep, real-time insights

Contact centers incorporating GenAI capabilities are revolutionizing self-service by delivering context-aware, human-like interactions that go past scripted responses. Unlike traditional solutions, contact centers powered by capable state-of-art large language models (LLMs), trained on enterprise data, can better understand customer intent, retain long-term context, and provide real-time, personalized support.

With AI21’s Jamba—a hybrid state-space and transformer LLM—GenAI can provide advanced sentiment analysis, low-latency response times (<500ms), and deep contextual understanding that adapts as the conversation evolves. NVIDIA® NeMo™ can be used to customize the models with domain knowledge. Once in production, model performance can be maintained with NVIDIA® NeMo™ microservices to curate new business data and user feedback, fine-tune and evaluate the model, connect with Retrieval-Augmented Generation (RAG) pipelines, and guardrail the model’s responses. Furthermore, NVIDIA® NIM™ can help scale latency and throughput, optimizing the delivery of GenAI-driven insights. 

GenAI-powered self-service also ensures seamless omnichannel experiences, enabling smooth transitions between chat, voice, and video interactions while maintaining context. By integrating intelligent automation and real-time insights, banks and insurers can provide faster, more relevant support—boosting efficiency while strengthening customer loyalty.

With real-time transcription and GenAI-driven insights, agents receive instant customer context and recommended responses, allowing them to resolve inquiries more efficiently. This technology also automates post-call work, generating summaries of key details and next steps—tasks that traditionally take several minutes per interaction. As a result, financial institutions can increase overall agent productivity by an average of 14% and by 34% for novice or lower-skilled workers, optimize call handling times, and empower agents to deliver faster, more personalized service.

Simplifying AI and GenAI adoption with a Contact Center-as-a-Service platform

A Contact Center-as-a-Service (CCaaS) platform streamlines the adoption of AI and GenAI capabilities by eliminating the need for complex infrastructure. With plug-and-play integration, firms can rapidly deploy AI-driven real-time speech recognition and GenAI-powered agent assistance without disrupting existing workflows.

Zuqo’s CCaaS platform can be used to accelerate deployment by orchestrating automation, live-agent interactions, and post-call workflows in a seamless environment. This allows firms to enhance customer engagement and agent productivity without the need for major technology overhauls.

With built-in scalability, Zuqo’s platform supports multi-region and multi-language operations, enabling financial institutions to expand without technological bottlenecks. Its API-driven architecture ensures effortless integration with existing CRM, compliance, and fraud monitoring systems, allowing AI-powered enhancements to fit naturally within current workflows.

Use case: Rapid fraud resolution in financial services

A customer calls their financial institution’s contact center after noticing an unauthorized charge on their credit card account. Instead of navigating frustrating hold times or being transferred multiple times, they are quickly connected to a live agent equipped with AI and GenAI-driven support tools.

As the customer explains the issue, real-time speech recognition transcribes the conversation, instantly analyzing intent and retrieving relevant account details. The GenAI-powered system assists the agent by surfacing next-best actions, allowing them to immediately credit the disputed amount while the fraud investigation takes place. With a single click, the agent efficiently cancels the compromised card and issues a new one, ensuring a swift resolution without requiring the customer to call back or complete additional steps.

Once the call ends, AI-driven quality assurance automatically reviews the interaction within 150 seconds, assessing over 60 compliance and service quality indicators to ensure a high standard of support.

The future of contact centers is finally here

The financial services industry is undergoing a profound shift, where traditional contact center models can no longer keep pace with rising customer expectations and increasing operational inefficiencies. AI and GenAI-powered solutions provide the opportunity to transform these challenges into competitive advantages, enabling banks and insurers to deliver faster, smarter, and more seamless customer experiences.

By integrating GenAI-driven self-service, real-time speech recognition, and automated workflows, financial institutions can enhance agent productivity, improve fraud resolution, and ensure regulatory compliance—while reducing costs. Contact Center-as-a-Service platforms make this transformation even more accessible, providing a scalable and easily integrated solution that eliminates the barriers to AI and GenAI adoption.

As the demand for efficiency, security, and personalized service continues to grow, financial institutions that embrace contact centers powered by AI and GenAI will position themselves as industry leaders. By modernizing their approach, banks and insurers can future-proof their organizations in an increasingly digital world.

We cannot have a sustainable energy system without storage, and lots of it. For signatory countries to achieve the commitments set at COP28, for example, global energy storage systems must increase sixfold by 2030.

Batteries are expected to contribute 90% of this capacity. They also help optimize energy pricing, match supply with demand and prevent power outages, among many other critical energy system tasks. Put simply, batteries are the beating heart of the energy transition – so what are the opportunities and challenges of this pivotal market? To find out, the Capgemini Research Institute surveyed 750 senior executives globally, including in the energy and utilities sectors. Its research report, The battery revolution: shaping tomorrow’s mobility and energy, generally reflects what I hear from clients, but I would add a couple of other factors.

As battery prices fall, their prevalence goes up

Let’s start with the good news: the falling price of batteries as production capacity increased over the past decade or so. It will be interesting to see how these ongoing price drops impact two of the challenges highlighted in the report – the extended payback period for investors and the profitability of manufacturers.

Likewise, as batteries become more available and affordable, I believe that every solar photovoltaic site will have some form of battery storage. I also expect that we will see more residential battery use. That could be people buying their own battery energy storage system (BESS) to capture energy from their solar panels and discharge it at peak times. Or it could be EV owners with Vehicle-to-Load (V2L) functionality renting or leasing a battery through the growing trend for Batteries-as-a-Service (BaaS).

Innovation could lead to surplus batteries and energy demand outstripping supply

This rise in availability, and corresponding drop in cost, also has its downsides. First, with so many batteries coming on to the market, how can we track where they are all are, or how well they are cared for during their life cycle? And what happens when an innovation arrives like “Flash Charging”, from the Chinese company BYD, which allows an EV battery to charge in 15 minutes? How do we know that the stock it supersedes will be properly disposed of, not just thrown into landfill?

Second, if increasing numbers of homes have a BESS and an EV charging point, it will create a level of demand that the grid was not designed to meet. For example, it makes sense to charge your EV overnight. But if everyone does the same, it puts a huge stress on the system. This could conceivably lead the Distribution System Operator (DSO) to tell consumers they can’t have an EV charger or a BESS – the political implications of which could be huge.

One final issue I would call out, which is also in the report, is the growing need for sovereignty in battery and energy production. We have all seen the impact on energy prices of being overly reliant on other countries for our supply. But having just one country – China – produce 83% of the world’s batteries, and mine around of one fifth of its lithium, creates similar vulnerabilities. Only time will tell how the trade tariffs the US administration recently imposed on China will affect the price of batteries and their raw materials stateside, for example.

Data and AI will be a big part of any solution

Currently, there is no single, defined solution to the issues I have described. But here are some ideas to consider.

1. In today’s distributed energy system, data and AI are king – so why not use them to help ease the stresses I have described? For example, advanced battery management systems can extend the life of batteries by constantly monitoring and maintaining their health and optimizing the way they charge and discharge. Data and AI can also speed up research and increase visibility of the supply chain.
2. Collaboration between governments and industry could help to overcome the issue of an overstrained power grid. For example, Great Britain’s energy regulator, OFGEM, has tasked the UK’s National Energy System Operator (NESO) with coordinating the delivery of a data sharing infrastructure (DSI) for the sector (until 2028). Having a DSI in place will ultimately make it easier to connect all the battery storage devices on the grid and optimize when and how they are used.
3. Europe and the US have invested heavily in rebalancing the scales for battery production. (Though again, it remains to be seen how the tariffs imposed by the US will affect its own efforts.) Meanwhile, governments that are serious about sovereignty will need to keep incentivizing local production. That’s as well as supporting the research and development of technologies that rely on different components or materials, like solid-state and sodium-ion batteries.
4. As I said before, new innovations risk making old ones redundant. Yet according to our report, just one in three organizations has taken meaningful steps to establish a circular economy. To move the dial, we need more initiatives like the EU Sustainable Batteries Regulation, more research into recycling and repurposing methods and a ‘sustainable-by-design’ approach to battery manufacture.

Batteries are not the only storage

These challenges will not be solved overnight. But meanwhile, other innovations are emerging. For example, the giant solar array at New York City’s John F Kennedy International Airport will also feature 3,84 MW of hydrogen fuel cells. I’m sure London’s Heathrow Airport would have appreciated this capacity when a fire in one of the electrical substations supplying its power forced it to close for over a day in March 2025.

Far from being the be all and end all, then, batteries are part of a bigger picture of energy storage – one that is constantly evolving. In future, this could mean we have a sustainable energy system that deploys different types of storage to help it manage, monitor and optimize energy use. With AI technologies developing at pace, we have more opportunity than ever to achieve it.

Beauty products can now be both glamorous and green. The EcoBeautyScore aims to make the industry’s environmental impact visible

Capgemini Invent is proud to support the official launch of the EcoBeautyScore – a major step forward in enabling the cosmetics industry to transparently communicate to consumers the environmental impact of their products and monitor it within the competition landscape.

Developed through unprecedented industry collaboration, this science-based and user-friendly digital tool empowers beauty brands (large and small) to evaluate, compare, and improve their product footprint across the entire lifecycle.

The scoring system is due to be launched by the EcoBeautyScore Association in Q2 2025 thanks to the collaboration of over 70 cosmetics companies and trade associations from four continents over three years. It provides a harmonized environmental scoring system based on the European Commission’s Product Environmental Footprint (PEF) methodology and is tailored specifically to the unique features of cosmetic products.

From vision to action: building a global sustainability alliance

Capgemini Invent has played a pivotal role in shaping the EcoBeautyScore initiative since its inception in 2021. Acting in a startup-like, agile environment, the Capgemini team supported the design, launch, and scale-up of the EcoBeautyScore initiative, helping build the EcoBeautyScore consortium’s operational backbone, technical governance, and digital foundations.

From defining the consortium’s vision and recruiting global stakeholders to coordinating technical working groups, leading communication and branding efforts, and guiding IT tool development and prototyping, Capgemini Invent has been at the heart of this three-year journey in collaboration with EcoBeautyScore partners.

“We are extremely proud to have accompanied this transformative initiative from the ground up. EcoBeautyScore is not just a tool, it’s a new way of thinking about sustainable product design and consumer transparency.”

Claire Lavagna, VP | Consumer Product Industry, Capgemini Invent

Plug-and-play access to environmental scoring

Initially covering four beauty categories (shampoo, conditioning hair treatments, body wash, and face moisturizers and treatments), the EcoBeautyScore enables brands to input product data in a user-friendly tool and instantly receive detailed impact results across 16 PEF indicators, including climate change, water usage, and land use. It provides actionable insights that inform eco-design strategies and facilitate benchmarking against comparable products.

Critically, the methodology has been reviewed by independent lifecycle assessment experts and is being validated by E&H, part of the certification group, EcoCert. This is to ensure alignment with ISO standards and PEF.

Empowering consumers, driving change

Today, more conscientious consumers want detailed cosmetic ingredients analysis. Recognizing this, Capgemini and the EcoBeautyScore Association joined forces to deliver on higher expectations. By the end of 2025, consumers in Europe will start seeing EcoBeautyScore on their cosmetic products. The score offers a transparent, standardized reference to help consumers make more informed, sustainable choices. The initiative aims to progressively expand its cosmetic analysis to additional beauty product categories and geographies, establishing a new global reference point for sustainability in the beauty sector.

About EcoBeautyScore Association  

The EcoBeautyScore Association is a not-for-profit organization whose primary goal is to develop a common environmental impact scoring system for cosmetic products, thus enabling consumers to make more informed purchasing decisions. Moreover, the Association aims to enable the industry to anticipate emerging regulatory changes, as well as foster a culture of eco-design among the members and beyond.

Marketing guru Shep Hyken once said “make every interaction count, even small ones.” This quote has always stuck with me because it’s so human, and because it explains why we feel a strong emotional connection to certain brands. We are more likely to become repeat customers if we experience good customer service, even in a small interaction.

It is well known that contact center agents are the face of any bank. They are on the front lines dealing with customer interactions and shaping your bank’s perception. Alas, the unfortunate reality is that today’s customer service isn’t standing up to customers’ needs. Consumers in 2025 expect more, and it’s on banks to step up.

Today’s consumer won’t stand for generic banking – they expect a personalized, seamless experience. More than that, they want it to feel human. Often, this demand lands with the staff at a contact center. But can we expect this staff to keep up with ever-growing customer expectations unaided? Or, even worse, can we expect the contact center to deliver a great experience when the perception is that banks are actively trying to automate away their jobs?

Capgemini’s World Retail Banking Report 2025 finds that only 16% of agents appear satisfied with their jobs. Attrition continues to rise, increasing the cost of recruitment and time spent training agents. In between, customers are looking for empathy in basic interactions – and instead find things impersonal and procedural.

I’m convinced we’ll do right by customers if we deploy technology to help overworked agents. Technology, after all, is a tool. The use of AI can help eliminate friction and let these agents deliver the kind of frictionless experiences that customers are hungry for. By implementing predictive AI capabilities, banks can prevent issues before they even occur based on historical patterns and trends, reducing the number of complaints and anomalies in real-time.

In the World Retail Banking Report, we sought to understand how 8,000 millennial and Gen Z customers view perhaps the single most important feature of their banking relationship: the card. The consensus was clear: there is room for improvement at every point of the customer journey. And there is a clear need for personal connection.

The worrying part of our research findings was the extent to which bank teams seemed aware of dissatisfaction among customers. Consider this: 68% of banking institutions acknowledged poor customer satisfaction as a major issue. What’s more concerning is that over 60% of bank marketing staff say they are overwhelmed by the number of applications they receive, and many banks acknowledge the KYC process can take days.

All of this is taking place against a backdrop of profound technological change. These changes have benefited nimble, digital-first players such as Monzo and Revolut. While they may seem small compared to the scale of US megabanks, they have succeeded in capturing valuable market niches. They did so by creating smooth digital experiences, broadening the aperture of services available and sidestepping much of the friction that can hinder established banks. They created real customer connection.

AI can let US banks build this connection too, removing bottlenecks in manual processes such as card applications. At a strategic level, it can inform banking strategy, create products with in-built personalization and close the customer service gap with the emerging neobank players.

By proactively predicting and addressing trends, the technology can assist banks in staying ahead of customer complaints and operational bottlenecks, making the process smoother for both agents and customers. 

However, AI can’t do it alone – many customers will still want the option to connect with a human being. After all, personal finance is personal, whether it’s a customer loan application or resolving a disputed charge. But AI can empower those humans, giving them a better insight into the customer’s situation and request.

For example, if a customer is angry about an unauthorized credit card transaction, a human agent augmented by AI can use sentiment analysis to detect the customer’s anger. The AI can then direct the query to an agent who has a high success rate in managing similar complaints and calming frustrated customers. AI can even proactively anticipate scenarios to help agents better serve customers.

Furthermore, by automating routine inquiries, AI allows agents to focus on complex, high-value tasks that require empathy, creativity, and judgment – attributes that customers are increasingly expecting. In this way, AI enables agents to provide more personalized service at scale, bridging the gap between human empathy and efficiency.

To put it simply, AI can make customer service agents much happier and more productive in their work. This takes more than a technology strategy: bank leaders will have to implement a thorough change management plan. That means educating employees about the potential of AI and their role in augmenting human capabilities, as well as clearly delineating what work will be done entirely by AI, and where AI will play a supporting role.

It’s also crucial that banks adopt a customer-centric AI strategy, focusing not only on operational efficiencies, but also how these technologies can directly enhance customer experience and employee experience. AI’s role is not just to solve problems faster, it’s to solve them better and with more empathy, while providing seamless self-service options and empowering agents to be more competent with contextual insights and continuous learning.

The bottom line: bank executives must push the boundaries of innovation to explore the potential of AI – in a safe and controlled fashion – that strives to deliver enhanced client engagement. It’s time to make every interaction count.

A revolution in trade and transaction reporting 

With rising regulatory pressures and data challenges, financial institutions increasingly demand efficient trade and transaction reporting. In response, there is a noticeable shift toward managed services solutions, with India emerging as a key destination. 

India’s rise in this domain can be attributed to several factors, including a robust talent pool, cost advantages, technological innovation, and operational efficiency, organizations like Capgemini are playing a central role in transforming how trade and transaction reporting is managed. In today’s financial landscape, institutions are under mounting pressure to meet increasingly complex and evolving regulatory requirements. From trade and transaction reporting (TTR) to data reconciliation, regulatory submissions, and analytics, the operational burden is growing – and so are the associated costs. Compliance is no longer just a legal necessity; it’s a strategic imperative that demands specialized expertise, scalable infrastructure, and round-the-clock operational support. 

These challenges are compounded by the need for agility, accuracy, and cost-efficiency. Financial institutions must navigate a web of jurisdictional rules, manage vast volumes of data, and ensure timely reporting – all while keeping operational costs in check. This is where India’s value proposition becomes particularly compelling. 

India has rapidly emerged as a global hub for managed services, offering a unique blend of deep domain expertise, advanced technological capabilities, and cost-effective delivery models. Its workforce is not only technically proficient but also increasingly specialized in financial services operations, regulatory compliance, and digital transformation. 

At Capgemini, we are leveraging this strategic advantage through our Post-Trade Transaction Reporting Practice. By expanding the scope of managed services beyond reporting, Capgemini is helping financial institutions transform compliance from a cost centre into a source of strategic value. This blog explores how India is not just supporting this shift – but leading it 

Leveraging a vast talent pool 

India has long been recognized for its diverse and highly skilled workforce, and the financial services sector is no exception. With a large pool of professionals possessing a unique blend of expertise in finance, technology, and regulatory compliance, India is increasingly recognized for its ability to manage complex reporting requirements. These professionals bring a strong understanding of global financial markets, regulatory standards, and the technologies required to handle large-scale data processing, making India an ideal base for supporting trade and transaction reporting needs. 

For financial institutions, this talent pool offers deep expertise in navigating regulatory landscapes such as MiFID II, EMIR, Dodd-Frank, and SFTR. These frameworks demand stringent data reporting and reconciliation processes.  This is an area where India’s workforce excels. 

Cost-effectiveness and operational efficiency 

In addition to technical expertise, India offers a significant cost advantage, making it an attractive destination for financial institutions aiming to optimize operational costs. Institutions are under constant pressure to streamline processes and reduce overhead while maintaining compliance and reporting accuracy. Leveraging managed services in India can significantly lower operational costs, as labor expenses are considerably lower than in many Western markets. 

Moreover, the cost-effectiveness extends beyond just labor. Infrastructure and technology investments in India can be more easily scaled, allowing financial institutions to adopt cutting-edge solutions at a fraction of the cost. This provides access to best-in-class capabilities without the need for substantial capital expenditures. 

Technological innovation and automation 

India is increasingly becoming a global leader in IT infrastructure and innovation, with a focus on technologies transforming the trade and transaction reporting landscape. At Capgemini, there is a strong emphasis on integrating advanced technologies such as automation, data analytics, and AI into managed services offerings. 

AI and machine learning streamline data aggregation, reconciliation, and validation, significantly reducing manual errors and improving speed and accuracy. These technologies enable financial institutions to achieve shorter turnaround times, ensuring that they meet regulatory deadlines and respond quickly to market changes. 

As adoption of these technologies accelerates, India is becoming a key player. By partnering with Indian service providers, financial institutions can stay ahead of regulatory and technological curves as well as emerging market trends. 

24/7 operational capabilities

Financial markets operate continuously, requiring reliable, round-the-clock support for reporting functions. India, with its well-established infrastructure, offers a 24/7 operational model, ensuring financial institutions meet their reporting obligations across time zones. 

Indian teams offer continuous monitoring and rapid response. This uninterrupted support is critical for global financial institutions with operations in multiple regions, ensuring seamless compliance and reporting activities across different time zones. 

Post-trade transaction reporting and specialized expertise

One of the key areas in which India excels is in post-trade transaction reporting. This includes critical processes like data reconciliation, regulatory submissions, and compliance checks that ensure transparency and reduce market risks. By focusing on building specialized talent pools, including subject matter experts, India enables firms to navigate the complexities of global regulations, such as EMIR and the U.S. Dodd-Frank Act. 

Capgemini, for example, has established a dedicated Post-Trade Transaction Reporting Practice that helps financial institutions optimize operations by streamlining these processes. Using advanced analytics, automation, and regulatory expertise it helps clients reduce risk and ensure compliance. Centralizing these delivers cost-effective, high-quality services vital to managing regulatory obligations. 

Regulatory change management   

As financial regulations evolve globally, institutions must be agile and adapt their systems and processes in real time. Regulatory change management is a key area where Indian managed services providers add value. Changes in regulatory frameworks can be complex and costly to implement, particularly when new rules require re-architecting internal systems or updating reporting platforms. 

Capgemini offers specialized solutions to help financial institutions navigate these changes. Whether it’s adapting existing systems to meet new regulations or developing entirely new platforms for reporting, Capgemini supports its clients through every phase of the change management process. This proactive approach ensures that financial institutions remain compliant with evolving regulations while avoiding costly penalties or operational disruptions. 

Conclusion

India’s emergence as a hub for trade and transaction reporting managed services reflects a broader shift toward outsourcing and automation in the financial services industry. With a wealth of talent, cost advantages, and a strong focus on technological innovation, India is transforming the way financial institutions manage regulatory compliance and reporting.