How to accelerate customer processes, reduce the volume of manual exceptions and simultaneously maintain stronger risk, compliance and operational control
The operating models and regulatory risks of banking, payments, lending, investment, insurance and other financial services differ, so their digitalisation priorities are analysed separately.
Insurance value is created by accurately assessing risk, swiftly administering claims and retaining the customer throughout the contract period.
Core banking operations are already digital, but the greatest untapped value lies in seamless customer processes, real-time risk control and modular core system modernisation.
Financial intermediaries create value by helping clients select the right product from several suppliers, therefore digitalisation must standardise data and partner exchanges without eliminating consultative trust.
Digital value arises from faster client service, more accurate portfolio control and a reliable data chain for investment decisions and reporting.
The greatest value is created by shortening the path from customer need to a reliable financing solution and actively managing portfolio risk.
In this business area, the product itself is a technology platform, so value arises from real-time payments, APIs, automated input, fraud control and reliable scale.
Developing new products, partner integrations and customer journey improvements takes time, whilst the cost of change continues to rise.
Connect customer channels, products, transactions, documents, decisions, compliance checks, fraud signals and service history so that standard cases proceed quickly whilst exceptions are clearly managed.
Select a customer onboarding, application, claims or service scenario and connect the standard path, human review, documents, controls and customer notifications.
Sector leadership is determined by the ability to simultaneously increase service speed, reduce manual work and maintain clear, auditable control.
The full digital sector analysis is presented below. A more detailed analysis tailored to the specific operating model is available on the business area pages.
The sector includes organisations that safeguard, transfer, lend, invest, insure or otherwise manage client funds and risk. Operating models differ, but all these organisations depend on high transaction volumes, data-driven decisions, strict regulatory oversight and uninterrupted service delivery.
A payment, account, credit, investment or insurance contract exists as a combination of data, rules and commitments.
Payments, customer actions, risk signals, contract changes, claims notifications and service requests are continuously processed.
Credit, insurance, investment, anti-money laundering and consumer protection decisions must be explainable and auditable.
Disruptions, data breaches or incorrect decisions can quickly cause financial losses and reputational damage.
Mobile applications, self-service and APIs often depend on legacy core systems and complex integrations.
Customer, transaction and behavioural data help improve services, but raise privacy, discrimination and model governance risks.
Sector changes are shaped by instant payments, more open financial ecosystems, cloud and API architectures, AI expansion, increasingly sophisticated fraud and stricter operational resilience requirements. Organisations are moving from individual automation projects to consistent management of data, models, third parties and technology risk.
Client data, decisions, controls and documents are managed in different systems, email and spreadsheets.
Basic transaction processing is digital, but client processes and controls have many manual exceptions.
Clients use apps and self-service, but more complex processes break down or require manual data transfer.
Core client journeys, data, decisions and exceptions are managed through a common integration and process layer.
In key processes, client, transaction, risk and servicing data are used together, and models and controls are regularly monitored. Coverage is not yet consistent across all products and exception processes.
Processes, products and models are continuously improved based on outcomes, whilst risk, control, suppliers and operational resilience are managed as one system.
The finance and insurance sector is already one of the most digitalised, so the next wave of value will not come from just another app or self-service feature. The greatest benefit lies in deeper integration of data, decision-making and control processes.
When customer, product, transaction, document, risk and service data remain in different systems, the standard process appears digital, but more complex exceptions still move to email, spreadsheets and manual coordination.
It is worth first selecting one high-volume process, organising its data, states, responsibilities and integrations, and only then extending AI or more advanced risk models.
Product, contract, pricing and operational logic is implemented in legacy, tightly coupled systems where even a minor change requires extensive analysis and testing.
Accounts, policies, credits, investments, documents, risk data and service history are stored in different systems.
Identity, document, beneficial owner, risk, sanctions and other verification data is obtained from multiple systems, whilst more complex cases are handled via email or spreadsheets.
Data collection, model output, expert assessment, approvals and documents are managed in different systems.
Payment, identity, device, behaviour, claims and customer history signals are not always analysed as a single real-time view.
Data for regulatory reports and controls are collected from different systems, and evidence is often prepared by staff only at the time of an audit or inspection.
A customer may initiate an action via self-service or an app, but a more complex case is transferred to a phone call, email or branch without preserving the full context.
Information on contracts, critical services, incidents, tests, data location and exit plans is held in different functions.
Notifications, documents, photographs, expert opinions, insurance coverage and claims decisions are checked across multiple channels.
Model data, versions, explanations, tests, bias assessments and usage limits are managed according to inconsistent rules across different teams.
Connect customer channels, products, transactions, documents, decisions, compliance checks, fraud signals and service history so that standard cases proceed quickly whilst exceptions are clearly managed.
Customer information is fragmented across products and systems
Customers are asked the same questions repeatedly, offers are less accurate, and service and risk decisions are based on incomplete context.
Customer identity, consent and unified profile platform
Integrates customer, entity, beneficiary, identity, document, consent, product, risk and service information for all channels.
Customer onboarding and KYC processes retain many manual exceptions
Customer identity, consent and unified profile platform
Integrates customer, entity, beneficiary, identity, document, consent, product, risk and service information for all channels.
Digital customer processes often break down at complex exceptions
Customer identity, consent and unified profile platform
Integrates customer, entity, beneficiary, identity, document, consent, product, risk and service information for all channels.
Credit, insurance and other decision processes are fragmented
Decisions are slow, their rationale is difficult to trace, and manual exceptions create inconsistent customer experience and control risks.
Digital process and decision management platform
Manages application, credit, policy, claims and other process data, rules, model outputs, human review, approvals and exceptions.
Customer onboarding and KYC process retains many manual exceptions
Digital process and decision management platform
Manages application, credit, policy, claims and other process data, rules, model outputs, human review, approvals and exceptions.
Insurance claims administration requires substantial manual work
Digital process and decision management platform
Manages application, credit, policy, claims and other process data, rules, model outputs, human review, approvals and exceptions.
Solutions must be implemented sequentially: first, customer, product and transaction data are standardised, then the entire process is digitalised with exceptions, and AI is used only with clear model, control and human accountability rules.
Integrates customer, entity, beneficiary, identity, document, consent, product, risk and service information for all channels.
Manages application, credit, policy, claims and other process data, rules, model outputs, human review, approvals and exceptions.
Connects transaction, identity, device, sanctions, behaviour, case and investigation data in real time and prioritises alerts by risk.
Connects claim notification, document and image collection, protection verification, expert tasks, fraud control, decision and payout.
Creates a managed real-time data and services layer between core systems, channels, partners, risk and compliance solutions.
Manages model registry, data lineage, versions, validation, explainability, customer group impact tests, performance monitoring and usage approvals.
Manages control rules, evidence, obligations, reports, changes and audit history directly within core processes.
Connects critical services, systems, suppliers, dependencies, incidents, tests, recovery and exit plans in line with DORA principles.
Controls, explanations, audit evidence and human review must be part of the process from the first version.
It is usually safer to first build a process and integration layer, and separate legacy system functions in stages.
It must be known where the data came from, when it was updated, what its quality status is and on what basis it may be used.
Manual work usually remains in complex exceptions, so clear human review, service continuity, data recovery and safe fallback processes are required.
Model versions, explanations, monitoring, supplier incidents, concentration risk and exit plans must be managed alongside internal systems.
Select a high-volume process and identify its data, systems, manual exceptions, control points and business impact.
Establish unified client, product and transaction identification and reliable data exchange between channels and core systems.
Digitalise one application, customer onboarding, claims or servicing scenario from start to finish, including manual exceptions.
Embed real-time risk signals, control evidence and common case management into the core process.
Expand proven architecture to other products, channels and partners, progressively decouple legacy system functions and scale managed AI and risk models only with reliable data.
Measure how many customer processes do not get stuck in a manual or other channel.
Assess the speed of identity, know-your-customer (KYC) and decision processes.
Identify gaps in data, rules and processes.
Assess the speed of the insurance claims process.
Assess fraud control accuracy, customer friction and investigator workload.
Measure self-service adoption and service shift.
Measure technological and operational resilience.
Assess architecture and process agility.
Advanced solutions can create significant value, but their use must be auditable, explainable, secure and proportionate to the decision risk. Models with significant impact on the customer must have clear human oversight capability.
Models analyse transaction, device, identity, behaviour and relationship data to detect complex anomalies.
Models combine more internal and external data to assess risk, pricing and most suitable terms.
AI helps search procedures, draft responses, summarise cases and explain complex financial information more clearly.
Payments, financing, insurance or account information via secure APIs are integrated into other companies' customer processes.
Controls, data quality, model usage and process deviations are monitored continuously, rather than only during periodic audits.
Federated learning, synthetic data and other technologies enable the creation and testing of models whilst reducing the disclosure of original customer data.
We will review how clients move through the process, which systems support decisions, where risk and compliance controls intervene, and help select the first step whose benefit can be clearly measured.
