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BCN have a heritage of delivering outcomes through our cloud-first services and currently support over 1200 customers across specialist sectors.
About Us
Your tech partner
AI for insurance organisations should deliver practical value. BCN helps insurance firms improve operations, manage risk and adopt AI securely with Microsoft technology.
AI READINESS
Insurance firms are balancing growing service demand, high operating costs and tighter regulatory expectations, often across disconnected systems and manual workflows. BCN helps insurance leaders across claims, underwriting, risk, compliance and client experience apply AI where it can create measurable value. Our wider AI for financial services expertise helps insurers improve performance while maintaining clear control over risk, compliance and customer outcomes.
Claims teams need to assess documents, confirm policy details, identify exceptions and keep customers updated, often while managing high caseloads. AI can help triage new claims, summarise evidence and route work to the right handler. This can shorten handling times, reduce avoidable administration and give experienced colleagues more time for complex cases that require judgement or customer care.
Underwriters must bring together policy information, risk data and supporting evidence before reaching a defensible decision. AI can organise this information, highlight missing details and prepare consistent recommendations for review. Used within agreed rules, it can support faster quote turnaround and more consistent underwriting while leaving accountability and final decisions with qualified people.
Compliance teams face growing volumes of regulation, reporting and evidence requests. AI can help review records, monitor agreed controls and prepare information for regulatory reporting. Clear source references, logs and approval stages allow teams to check how outputs were produced, correct mistakes and retain the evidence needed for internal review, customer challenge or regulatory scrutiny.
Clients expect quick, accurate answers throughout quotation, policy servicing and claims. AI can help service teams retrieve policy information, draft responses and provide timely status updates through approved channels. This reduces delays and supports a more consistent experience, while sensitive queries, complaints and decisions with material customer impact continue to be handled by trained colleagues.
Insurance firms can use AI to improve audience insight, identify potential opportunities and support more relevant marketing activity. AI can analyse customer, policy and engagement data to highlight trends, segment audiences and help teams prioritise suitable prospects or broker relationships. It can also support campaign planning, content creation and lead follow-up across approved channels. With clear data controls, compliance checks and human review, insurers can use these capabilities to improve marketing efficiency and generate new business.
AI OPPORTUNITIES
Insurance processes generate large volumes of documents, data and customer communication across the entire policy lifecycle. Claims handlers, underwriters, compliance teams and client service colleagues must find, review and act on this information accurately, often while moving between disconnected systems and completing repetitive administrative tasks. As workloads rise, these manual processes can slow decisions, increase operating costs and make it harder to deliver a consistent customer experience.
AI gives insurance firms an opportunity to reduce this pressure by helping teams retrieve information, summarise documents, classify cases and support routine workflows. It can also make valuable information easier to access across different business functions, helping employees respond more efficiently and make better-informed decisions.However, successful adoption depends on applying AI to a clearly defined operational need. Data quality, system access, security, accountability and human oversight must all be considered from the beginning. When these foundations are in place, insurers can use AI to improve efficiency while maintaining the accuracy, fairness and transparency expected by customers, brokers and regulators.
Claims files, policy documents, risk data and correspondence are often spread across several systems. Teams spend significant time finding, checking and rekeying information. AI can bring relevant material together and make it easier to review, provided access permissions and data quality are addressed first.
Higher case volumes and skills shortages place pressure on claims handlers, underwriters and service teams. AI can reduce repetitive reading, classification and administration, allowing people to focus on exceptions, judgement and customer conversations. The strongest use cases remove friction without removing appropriate human ownership.
Firms must be able to show how customer outcomes and material decisions are reached. AI adoption needs named owners, documented controls and clear review routes. These requirements become even more important when agentic AI for insurance can take actions across several stages of a workflow.
Policyholders and brokers expect faster answers, clearer updates and fewer avoidable delays. AI can support round-the-clock information access and quicker case progression, but speed cannot come at the cost of accuracy or fairness. Service design must include escalation routes for complex, sensitive or disputed matters.
EXAMPLES
The right starting point is a focused process with a clear problem, suitable data and an outcome that can be measured. BCN helps insurance firms compare opportunities by value, feasibility and risk before any build begins. This keeps investment focused on real operational needs and provides a controlled route from initial testing to wider adoption across claims, underwriting, compliance, policy administration and service.
AI can read incoming claim information, classify the case and identify missing documents or potential exceptions. Straightforward claims can be directed into a faster workflow, while complex or high-value cases are sent to experienced handlers. Measures such as time to first action, handling time and referral accuracy provide a clear view of whether the use case is working.
AI can gather relevant information from approved sources, compare it with underwriting criteria and prepare a structured summary. It can also flag gaps, inconsistencies or cases that need senior review. Underwriters retain responsibility for decisions, while spending less time on information gathering and more time assessing risk, considering exceptions and applying professional judgement.
Renewals, mid-term adjustments and document requests create high volumes of repeatable work. AI can help check submissions, populate approved fields, prepare policy documents and route unusual cases for review. This can reduce rekeying and speed up routine service without allowing an automated process to make changes outside agreed permissions or business rules.
AI can organise control evidence, monitor selected activity and prepare draft reports using approved data. Compliance teams can trace findings back to source material and review exceptions before anything is submitted. This supports more timely reporting, reduces manual collation and gives risk leaders a clearer view of where controls are working or need attention.
AI can analyse patterns across claims data, documents and previous activity to highlight cases that merit closer review. It should support investigation by highlighting cases for human review, with final decisions remaining with trained teams. Human review, tested thresholds and outcome monitoring help firms check accuracy, manage bias and protect fair treatment for customers.
AI assistants can help answer routine policy questions, retrieve approved information and draft consistent responses for service teams. More advanced AI agents can coordinate selected steps across systems within defined limits. Clear authentication, escalation and approval rules keep sensitive requests and material changes under human control.
OUR EXPERTISE
AI GOVERNANCE
AI in insurance must be trusted by customers, colleagues, auditors and regulators. Governance needs to be designed into each use case before it reaches live data or business processes. BCN helps firms define ownership, access, approval and monitoring controls around the intended outcome. This creates a clear operating model for adoption, with evidence available to show how the system works and where people remain accountable.
Teams need to understand which information influenced an AI-supported output and how it was used. BCN designs workflows that retain source references, decision context and clear explanations for reviewers. This helps claims, underwriting and compliance teams challenge outputs, correct errors and communicate the basis of decisions to customers, auditors or regulators where appropriate.
AI is only as dependable as the information and permissions behind it. We help firms identify approved data sources, review access rights, define retention requirements and apply controls around sensitive information. This reduces the risk of inappropriate access, poor-quality outputs and data being used for a purpose that has not been agreed.
Audit records should show what the system accessed, what it produced, which actions it took and who approved the outcome. BCN builds logging and evidence requirements into the workflow so activity can be reviewed later. This supports control testing, incident investigation and regulatory response without relying on users to reconstruct events manually.
Not every action carries the same level of risk. We help firms set approval points based on customer impact, financial exposure, confidence levels and regulatory obligations. Low-risk administrative steps may progress automatically, while claims decisions, underwriting exceptions and sensitive communications remain subject to review by an authorised person.
Microsoft identity, data protection, security and compliance services can provide a consistent control layer for AI adoption. BCN aligns new solutions with the firm’s existing Microsoft environment, access policies and monitoring. Our IT support for insurance companies can also strengthen the wider technology foundation on which secure, reliable AI services depend.
AI IN FINANCE
A successful AI programme for an insurance or financial services organisation needs a clear route from early interest to a secure, governed and measurable service. BCN begins by working with business and technology leaders to define the operational challenge, the intended outcome and how success will be measured.
The approach considers the wider environment around each use case, including existing systems, data quality, security requirements, regulatory responsibilities, user permissions and human oversight. This helps organisations identify practical opportunities while addressing the controls needed to use AI responsibly.
Each stage has a clear purpose, decision point and success measures. Leaders can assess readiness, test assumptions and control investment before committing to wider adoption. Solutions that demonstrate value can then be introduced through a phased roadmap covering integration, adoption, monitoring and continuous improvement.
We review the current process, technology environment, data quality, security controls and team capability. This identifies practical gaps that could affect delivery, including unclear ownership, broad permissions or unreliable source data. Leaders receive a clear view of what is ready now, what needs attention and which ideas carry unnecessary risk.
Potential use cases are compared against business value, feasibility, customer impact and regulatory risk. We focus on problems with a clear owner and outcome, such as reducing claim handling time or improving underwriting consistency. This prevents teams spreading investment across loosely defined ideas and creates a stronger case for an initial project.
BCN turns the selected opportunities into a phased plan covering data, integration, governance, adoption and measurement. The roadmap sets out responsibilities, dependencies and decision points, giving technology and business leaders a shared view of delivery. It also makes clear which controls must be in place before a use case can access live data.
We build and test the use case in a controlled setting with representative data, agreed guardrails and defined human oversight. Performance is measured against operational, risk and customer criteria before live use expands. Microsoft proof-of-concept and deployment funding may be available for eligible engagements, subject to assessment and programme availability.
Once a pilot has demonstrated value, BCN supports integration, user adoption, monitoring and service management. We expand the solution in stages, using evidence from live performance to guide each step. Successful components can then support adjacent workflows or governed AI agents without losing visibility of ownership, risk or outcomes.
WHY US
BCN combines insurance sector knowledge with Microsoft AI, data, security and managed service expertise. This allows us to consider the complete environment surrounding AI adoption, from the quality and accessibility of information through to integration, governance, regulatory obligations and user change.
We work with leadership, operational and technology teams to identify practical opportunities, define measurable outcomes and build the controls required for responsible delivery. Rather than treating implementation as a standalone technology project, BCN connects each solution to the organisation’s wider systems, processes and service priorities. This helps insurance firms prove value through focused delivery, manage risk and maintain clear ownership as successful uses of AI are introduced more widely.
Our Microsoft Copilot Specialisation reflects proven capability in preparing organisations for secure adoption, deployment and user change across Microsoft environments.
Our work across regulated industries and IT support for insurance firms helps us align technical delivery with operational risk, compliance duties and customer outcomes.
We connect AI design with the data, integration, security and governance work needed to move a valuable idea into day-to-day operations.
Each engagement is tied to defined measures, such as handling time, referral accuracy, administrative effort, compliance evidence or client service performance.
CASE STUDIES
Neil Paton
Director of Technology, Frenkel Topping Group
BCN were fundamental in helping us move to Microsoft Azure, Power BI and bespoke applications. The result was a massive improvement in efficiencies that transformed the business to provide better services.
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The rapid development of Frenkel Topping in a short time had presented several key challenges and issues for BCN to help them overcome. Their users’ technology requirements had grown accordingly in complexity and volume and they required an aligned technology stack to bring all the brands to the same technical standards.
Over the past four years Schofield Sweeney has partnered with BCN to deliver its ambitious IT roadmap, including its transition to Microsoft Azure, alongside enhanced user support and security to enable more agile working and free the IT team to explore AI innovation and automation.
Horwich Cohen Coghlan Solicitors found the IT partner they needed in BCN, and we are proud to continue supporting the firm along their digital transformation journey. Our in-house Microsoft experts provide a fully managed service around the Azure infrastructure, including through an Azure Advanced Managed Service and a Microsoft Defender Managed Service.
Book an AI discovery call or consultancy session to identify high-value use cases, understand readiness and agree a safe route towards measurable results.
