Explained: Digital Banking Ecosystem

Recently, IndusInd Bank added a new arsenal to its digital ecosystem. It launched Video Branch, which allows customers to communicate with their branch manager, relationship manager, or centralized video branch executive in real-time. While doing so, customers can even track all of their important financial transactions, such as initiating a fixed deposit or recurring deposit.

This is an excellent example of how building a digital ecosystem helps banks become more relevant to clients, allowing them to form stronger relationships and grab higher wallet shares.

According to Global Market Insights, the Digital Banking Market has crossed USD 8 trillion in 2020 and is expected to grow at a rate of roughly 5% from 2021 to 2027. This industry growth is due to consumers’ increased usage of mobile devices to accomplish day-to-day financial operations. Customers utilize digital banking to check their account balances, deposit checks, transfer funds and shop online. Furthermore, the expanding millennial generation (aged 16 to 34) is pushing banks to offer digital banking services and adopt a digital ecosystem strategy.

What is a Digital Banking Ecosystem?

Digital banking ecosystems are collaborations built on partnerships that use technology to provide new products and services to clients. The idea behind collaborative models like these is simple: although no single bank can cover all of its customers’ needs, a consortium of banks and digital companies can.

Collaborations with Big Tech companies such as Apple Pay and Google Pay have gained significant market share in the mobile wallet sector. Banks, on the other hand, are concerned that closer collaboration with these firms would become a Trojan horse, compromising their position.

There are three types of stakeholders in a digital ecosystem: banks, third-party service providers, and customers. The role of a third party is to act as an intermediary between the customer and the bank. However, customers have to give their consent to third-party providers to carry out financial transactions on their behalf.

Need for a Digital Banking Ecosystem

According to Everfi research, 53% of financial institution customers have moved on from their principal financial provider, with another 9% considering doing so. This explains why banks must evolve if they want to retain existing customers and perhaps attract new ones.

Regulation

Banks must now allow access to customers’ data and online payment capabilities using open API technology, with specific consent. Hundreds of thousands of new clients are introduced to the ecosystem each month as a result of open banking-enabled products being used by both users and enterprises around the world.

Increased Competition

FinTechs are taking advantage of the decreased barrier to enter into the financial services market by using their technical expertise and superior digital client experience. Due to the limited services now offered by these challenger banks, few clients have totally transitioned away from their former pillar banks.

Increasing digital and technological investments

Banks are aggressively investing in their technological systems and data both globally and locally. This lowers operating costs and provides a foundation for new revenue streams.

Changing customer behavior

A paradigm shift from the previous paradigm (provider-based) to the new paradigm (need-based) aided in the creation of one-stop-shop ecosystems to meet the wants and needs of customers. Banks can now embed their products and services at the place of need, such as embedded finance, in this new paradigm.

Over the next five years, customers expect their bank to provide them with more personalization, proactive services, and connected omnichannel experiences. COVID-19 has risen digital adoption levels, with 43 percent of worldwide respondents stating their banking habits have changed during the financial crisis.

The Benefits of the Digital Banking Ecosystem

Banks in digital ecosystems have a number of substantial advantages built-in, including strong customer relationships and well-known brands. Banks, their customers, and other stakeholders can all profit when such strengths are joined with third-party artificial intelligence (AI) and cloud-based solutions.

Increasing the reach and quality of digital products

Working with technology partners can help a bank extend its digital distribution, improve the quality of its products, and reduce client acquisition expenses.

Citibank is one of eight banks that have teamed with Google to offer Google Pay consumers digital checking and savings accounts. Citibank and others can use Google’s platform to deliver branded products and advice to digital-only clients as a result of the partnership.

Competencies in product development are outsourced

When third parties have the technology and capacity to do it better, banks may not need to develop and maintain best-in-class digital solutions and products.

M&T Bank has collaborated with LPL Financial, an investment advisor and independent broker-dealer, to provide access to LPL’s scalable platform, integrated processes, and differentiated product offerings to its brokerage and insurance advisors. M&T advisers can now focus on client connections while the bank tries to improve its efficiency and reinvests in core operations as a result of the agreement.

Providing other ecosystems with access to banking services

When customers transact in other ecosystems, they demand seamless access to bank-held data, as well as banking goods and services. Providing secure access to their systems to partners can help banks bridge ecosystem gaps and stay relevant to their clients.

Intuit’s QuickBooks uses an API technology that enables customers to better manage their financials, enabling them to access their accounts in a variety of banking and cash management functions in one place.

Providing other ecosystems with banking services

FinTechs frequently tout digital procedures and capabilities that can assist banks in providing good, frictionless, and efficient client experiences while avoiding costly updates.

PNC Financial uses OnDeck Capital’s digital onboarding process and external data sources to streamline its small business financing process. As the company claims, the agreement allows PNC to keep control over its risk appetite while reducing loan approval times from days to minutes.

Concentrating on fundamental competencies

By delegating non-core product and capability management to a third party, banks can maintain and strengthen customer connections while focusing resources on vital strategic priorities.

State Farm recently dissolved its banking, mortgage, and credit card divisions in order to focus on its core insurance business. Agents, on the other hand, continue to sell bank products to customers through their partnerships with those buyers, giving State Farm a one-stop-shop for all financial needs.

Product and service marketplaces are expanding

Ecosystems can enable a bank to disaggregate and securely market products and services to other institutions, resulting in increased revenue for the bank and value for the partner.

In the United States, HSBC has partnered with NepFin, an online commercial lending platform, to provide growth finance and international services to FinTech’s midsized business clients. The arrangement allows HSBC to reach out to digital customers it couldn’t previously reach.

Increasing the value of internal resources

Ecosystems are a cost-effective way for banks to promote their in-house developed capabilities to other banks, FinTechs, and even non-bank enterprises.

Banks and businesses can offer white-label versions of their products and services through banking-as-a-service (BaaS). BBVA will be able to commercialize its core banking functions, including payments, financing, identity verification, and account opening, as a result of the agreement.

Conclusion

Banks and financial institutions need to continuously upgrade the experience for their customers. However, while doing so they need to factor in the demographics of their customer base. While the millennials and GEN-Zs want services at their fingertips, the older generation still prefers visiting a physical office.

Banks will have to integrate new-age technologies such as AI, ML, and big data analytics into their processes to elevate customers’ experience and improve efficiency in operations. The key to success would be decoding data into actionable insights and acting in real-time. Furthermore, they need to train their workforce and help them get acquainted with news systems.

The next step in the growth of digital banking platforms would be to continuously engage, assist and educate customers accustomed to traditional banking methods. This will fastrack the revenue streams and profit in the near future.

Generic AI is no longer enough. Domain-specific AI is the new enterprise advantage.

From hospitals to factories to insurance carriers, organizations are learning the hard way: horizontal AI platforms might be impressive, but they’re often blind to the realities of your industry.

Here’s the new playbook: intelligence that’s narrow, not general. Context-rich, not context-blind.
Welcome to the age of domain-specific AI agents— from underwriting co-pilots in insurance to care journey managers in hospitals.

Why Generalist LLMs Miss the Mark in Enterprise Use

Large language models (LLMs) like GPT or Claude are trained on the internet. That means they’re fluent in Wikipedia, Reddit, and research papers; basically, they are a jack-of-all-trades. But in high-stakes industries, that’s not good enough because they don’t speak insurance policy logic, ICD-10 coding, or assembly line telemetry.

This can lead to:

Hallucinations in compliance-heavy contexts
Poor integration with existing workflows
Generic insights instead of actionable outcomes

Generalist LLMs may misunderstand specific needs and lead to inefficiencies or even compliance risks. A generic co-pilot might just summarize emails or generate content. Whereas, a domain-trained AI agent can triage claims, recommend treatments, or optimize machine uptime. That’s a different league altogether.

What Makes an AI Agent “Domain-Specific”?

A domain-specific AI agent doesn’t just speak your language, it thinks in your logic—whether it’s insurance, healthcare, or manufacturing.

Here’s how:

Context-awareness: It understands what “premium waiver rider”, “policy terms,” or “legal regulations” mean in your world—not just the internet’s.
Structured vocabularies: It’s trained on your industry’s specific terms—using taxonomies, ontologies, and glossaries that a generic model wouldn’t know.
Domain data models: Instead of just web data, it learns from your labeled, often proprietary datasets. It can reason over industry-specific schemas, codes (like ICD in healthcare), or even sensor data in manufacturing.
Reinforcement feedback: It improves over time using real feedback—fine-tuned with user corrections, and audit logs.

Think of it as moving from a generalist intern to a veteran team member—one who’s trained just for your business.

Industry Examples: Domain Intelligence in Action

Insurance

AI agents are now co-pilots in underwriting, claims triage, and customer servicing. They:

Analyze complex policy documents
Apply rider logic across state-specific compliance rules
Highlight any inconsistencies or missing declarations

Healthcare

Clinical agents can:

Interpret clinical notes, ICD/CPT codes, and patient-specific test results.
Generate draft discharge summaries
Assist in care journey mapping or prior authorization

Manufacturing

Domain-trained models:

Translate sensor data into predictive maintenance alerts
Spot defects in supply chain inputs
Optimize plant floor workflows using real-time operational data

How to Build Domain Intelligence (And Not Just Buy It)

Domain-specific agents aren’t just “plug and play.” Here’s what it takes to build them right:

Domain-focused training datasets: Clean, labeled, proprietary documents, case logs.

Taxonomies & ontologies: Codify your internal knowledge systems and define relationships between domain concepts (e.g., policy → coverage → rider).
Reinforcement loops: Capture feedback from users (engineers, doctors, underwriters) and reinforce learning to refine output.
Control & Clarity: Ensure outputs are auditable and safe for decision-making

Choosing the Right Architecture: Wrapper or Ground-Up?

Not every use case needs to reinvent the wheel. Here’s how to evaluate your stack:

LLM Wrappers (e.g., LangChain, semantic RAG): Fast to prototype, good for lightweight tasks
Fine-tuned LLMs: Needed when the generic model misses nuance or accuracy
Custom-built frameworks: When performance, safety, and integration are mission-critical

Use Case	Reasoning
Customer-facing chatbot	Often low-stakes, fast-to-deploy use cases. Pre-trained LLMs with a wrapper (e.g., RAG, LangChain) usually suffice. No need for deep fine-tuning or custom infra.
Claims co-pilot (Insurance)	Requires understanding domain-specific logic and terminology, so fine-tuning improves reliability. Wrappers can help with speed.
Treatment recommendation (Healthcare)	High risk, domain-heavy use case. Needs fine-tuned clinical models and explainable custom frameworks (e.g., for FDA compliance).
Predictive maintenance (Manufacturing)	Relies on structured telemetry data. Requires specialized data pipelines, model monitoring, and custom ML frameworks. Not text-heavy, so general LLMs don’t help much.

Strategic Roadmap: From Pilot to Platform

Enterprises typically start with a pilot project—usually an internal tool. But scaling requires more than a PoC.

Here’s a simplified maturity model that most enterprises follow:

Start Small (Pilot Agent): Use AI for a standalone, low-stakes use case—like summarizing documents or answering FAQs.

Make It Useful (Departmental Agent): Integrate the agent into real team workflows. Example: triaging insurance claims or reviewing clinical notes.
Scale It Up (Enterprise Platform): Connect AI to your key systems—like CRMs, EHRs, or ERPs—so it can automate across more processes.

Think Big (Federated Intelligence): Link agents across departments to share insights, reduce duplication, and make smarter decisions faster.

What to measure: Track how many tasks are completed with AI assistance versus manually. This shows real-world impact beyond just accuracy.

Closing Thoughts: Domain is the Differentiator

The next phase of AI isn’t about building smarter agents. It’s about building agents that know your world.

Whether you’re designing for underwriting or diagnostics, compliance or production—your agents need to understand your data, your language, and your context.

Ready to Build Your Domain-Native AI Agent?

Talk to our platform engineering team about building custom-trained, domain-specific AI agents.

Further Reading: AI Code Assistants: Revolution Unveiled