of organizations are already using AI in at least one business function
potential net cost reduction for banks through AI
US insurers are already running generative AI in their business
Why work with us
AI and Data Experts on board
Finished projects
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
What our clients say
Our approach was simple: automate the 70–80% of work that’s mechanical, and protect the 20–30% that requires judgment. That’s not a compromise, it’s good system design. Every attempt to push AI beyond its reliable boundaries is just another way of manufacturing technical debt.
Adam Komorowski – Meeting Tomorrow Senior Data Scientist at Addepto
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
Clients that trusted us
The integration of AI in finance and insurance is more than a technology upgrade; it requires strict control, robust safeguards, and disciplined compliance management across every solution. Fintechs must design AI systems that are auditable, explainable, and aligned with regulatory expectations so that risk, legal, and compliance teams stay firmly in charge of how models influence decisions. With the right governance, monitoring, and access controls, AI can enhance fraud prevention, risk management, and customer service without compromising security, data privacy, or regulatory obligations.
Addepto delivers these systems in close collaboration with your in‑house domain experts, using their process and regulatory knowledge to cut discovery time and reduce rework. This co‑design approach accelerates development from proof‑of‑concept to production while keeping AI solutions practical, controlled, and compliant from day one.
AI Solutions for Finance Sector
AI‑powered Risk & Fraud Analytics
AI for Underwriting & Credit Decisions
Claims, Servicing & Back‑Office Automation
AI‑driven Personalization & Advisory
How AI Supports the Finance & Insurance Sector
Higher return on capital and revenue growth
AI helps banks and insurers identify the most profitable customers, price credit and policies more precisely, and predict churn or delinquency risk earlier. By using propensity and risk models in credit decisions, renewals, and cross‑sell campaigns, institutions can lift approval quality, reduce loss ratios, and grow interest and fee income without loosening risk appetite.
Lower operating and remediation costs
Loan checks, identity verification, fraud screening, and reconciliations are time-consuming — and costly when they go wrong. AI handles this layer automatically, catching errors before they become problems. Operations teams spend less time fixing exceptions and more time on work that matters.
Stronger compliance and risk oversight
AI‑enabled surveillance can continuously scan transactions, communications, and positions against risk and regulatory rules, flagging suspicious activity or potential breaches in near real-time. With built‑in model documentation, data lineage, and explainability, banks and insurers can demonstrate how AI‑assisted decisions were made during audits and regulatory reviews, strengthening overall control.
More scalable, efficient front‑ and back‑office
AI copilots and decision‑support tools help relationship managers, underwriters, and claims handlers prepare cases, summarize client data, and draft recommendations in minutes instead of hours. Contact centers and digital channels can resolve routine servicing requests with virtual assistants, allowing specialists to focus on complex restructurings, large claims, or high‑value clients while maintaining consistent service levels as volumes grow.
AI in Finance and Insurance: Challenges AI Can Solve
How can AI help underwriters make faster, more consistent decisions?
Challenge: Underwriters and credit officers spend hours collecting data from multiple systems, reading documents, and applying complex rules manually. This slows down loan approvals and policy issuance, creates inconsistencies between teams, and makes it hard to scale without adding more people.
Solution: AI systems aggregate financial, behavioral, and third‑party data into a single view, score risk, and propose draft decisions with explanations. Underwriters keep control over final approval, but get pre‑filled assessments, scenario analyses, and rationales in minutes instead of hours – cutting turnaround times while improving portfolio quality.
How can AI reduce fraud losses without overwhelming risk teams?
Challenge: Traditional rules‑based systems generate huge volumes of fraud and AML alerts, most of which are false positives. Analysts lose time on low‑risk cases, real threats can be missed, and customers are frustrated by unnecessary friction and blocked transactions.
Solution: AI‑based anomaly detection and risk‑scoring models learn from historical cases and behavior across channels. The system prioritizes high‑risk alerts, suppresses noise, and surfaces explainable indicators for each flagged event, so investigators focus on the truly suspicious activity and resolve legitimate transactions faster.
How can AI speed up claims and everyday servicing?
Challenge: Insurance and banking service teams deal with large backlogs of claims, emails, and service requests, much of it repetitive and document‑heavy. Manual triage and data entry lead to long cycle times, inconsistent decisions, and rising operational costs.
Solution: NLP and computer vision read documents, extract key data, classify cases, and route them to the right workflows – or straight‑through processing when rules are met. AI copilots support agents with summaries, next‑best actions, and draft responses, reducing handling time per claim or request and improving customer satisfaction.
How can AI help finance teams stay ahead of regulatory demands?
Challenge: Producing regulatory reports and defending model‑driven decisions requires clean data, complete audit trails, and clear documentation of how decisions were made. Many institutions rely on spreadsheets and manual reconciliations, which are slow, error‑prone, and difficult to audit.
Solution: AI‑ready data pipelines and monitoring dashboards that track data lineage, model performance, and key risk indicators in one place. Automated checks highlight anomalies, generate draft narratives for reports, and maintain a history of model versions and decisions, helping finance, risk, and compliance teams respond quickly and confidently to regulators and internal audits.
How can AI help underwriters make faster, more consistent decisions?
Challenge: Underwriters and credit officers spend hours collecting data from multiple systems, reading documents, and applying complex rules manually. This slows down loan approvals and policy issuance, creates inconsistencies between teams, and makes it hard to scale without adding more people.
Solution: AI systems aggregate financial, behavioral, and third‑party data into a single view, score risk, and propose draft decisions with explanations. Underwriters keep control over final approval, but get pre‑filled assessments, scenario analyses, and rationales in minutes instead of hours – cutting turnaround times while improving portfolio quality.
How can AI reduce fraud losses without overwhelming risk teams?
Challenge: Traditional rules‑based systems generate huge volumes of fraud and AML alerts, most of which are false positives. Analysts lose time on low‑risk cases, real threats can be missed, and customers are frustrated by unnecessary friction and blocked transactions.
Solution: AI‑based anomaly detection and risk‑scoring models learn from historical cases and behavior across channels. The system prioritizes high‑risk alerts, suppresses noise, and surfaces explainable indicators for each flagged event, so investigators focus on the truly suspicious activity and resolve legitimate transactions faster.
How can AI speed up claims and everyday servicing?
Challenge: Insurance and banking service teams deal with large backlogs of claims, emails, and service requests, much of it repetitive and document‑heavy. Manual triage and data entry lead to long cycle times, inconsistent decisions, and rising operational costs.
Solution: NLP and computer vision read documents, extract key data, classify cases, and route them to the right workflows – or straight‑through processing when rules are met. AI copilots support agents with summaries, next‑best actions, and draft responses, reducing handling time per claim or request and improving customer satisfaction.
How can AI help finance teams stay ahead of regulatory demands?
Challenge: Producing regulatory reports and defending model‑driven decisions requires clean data, complete audit trails, and clear documentation of how decisions were made. Many institutions rely on spreadsheets and manual reconciliations, which are slow, error‑prone, and difficult to audit.
Solution: AI‑ready data pipelines and monitoring dashboards that track data lineage, model performance, and key risk indicators in one place. Automated checks highlight anomalies, generate draft narratives for reports, and maintain a history of model versions and decisions, helping finance, risk, and compliance teams respond quickly and confidently to regulators and internal audits.
AI Solutions in Finance
How do you catch fraud before the transaction clears?
AI fraud engines pull together live transaction streams, device signals, and historical fraud cases into a unified feature store, then run deep neural networks to score every payment, login, or policy change in milliseconds. Techniques like anomaly detection, graph analytics (to surface fraud rings), and sequence modelling (to catch behavioral shifts over time) power dedicated platforms. Risk scores and the key drivers behind them are exposed via APIs to your authorisation, AML, and case-management systems — so step-up verification, dynamic limits, or blocking decisions come with a clear, auditable reason for every flagged event.
How do you settle legitimate claims faster while keeping fraud out?
Incoming claims — regardless of channel — are processed through document classification, entity extraction, and damage assessment models without manual input. Complex cases are escalated automatically; straightforward ones proceed to straight-through processing. Contact-centre agents receive RAG-generated response suggestions and next-best actions within their CRM, ensuring compliant, consistent customer communication while reducing average handle time.
How do you stay audit-ready without burying your team in manual reporting?
Data quality checks and anomaly detection run automatically across your core systems, data warehouse, and reporting outputs — catching discrepancies before they reach a regulator. Every model in production is version-tracked with its training data and performance metrics, and explainability tools generate the reason codes needed for adverse-action notices and regulatory review. On top of that, AI assistants trained on your internal policies and past submissions help teams draft regulatory reports faster — with every output traceable back to the underlying data.
AI‑Augmented Underwriting and Credit Engines
AI models trained on historical credit data, policy exposure, and claims outcomes score each application for risk — estimating the likelihood of default or loss at both individual and portfolio level. These scores are presented alongside eligibility checks and pricing constraints, with a generative AI layer that drafts a plain-language rationale and suggested terms on the spot. Underwriters and credit officers see everything they need inside their existing system, can override any recommendation, and every decision is logged automatically for governance and audit purposes.
What our clients say
The entire team has so much to be proud of – driving up efficiency, quality and timeliness, and I (and the entire Medidata team) certainly appreciate the collaboration needed to achieve all of this.
Craig Stauffer – Medidata Senior Director, Implementation Services – Medidata Solutions
The collaborative culture during the project was unique and significantly facilitated seamless implementation. Addepto has completely satisfied our requirements.
Dawid Majcherek – Nexteer Advanced Technology Engineer – Nexteer Automotive
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
We are recognized as one of the best AI, BI, and Big Data consultants
We helped multiple companies achieve their goals, but - instead of making hollow marketing claims here - we encourage you to check our Clutch scoring.
FAQ
Where does AI bring the fastest ROI in finance and insurance?
Most organizations see early returns in high‑volume, rules‑based processes: credit pre‑screening, claims triage, fraud and AML alert prioritization, and customer self‑service. These use cases tap into data you already have, integrate with existing systems, and deliver measurable gains in approval speed, loss ratio, or handle time within a few months of go‑live.
How do you make sure AI models are compliant and explainable?
For regulated decisions (credit, pricing, underwriting, claims), we prioritize interpretable model families or layer explainability methods (e.g., feature importance, reason codes) on top of more complex models. Every model is wrapped with governance: version control, training‑data documentation, performance monitoring, and access policies, so risk and compliance teams can review, challenge, and approve how AI is used before anything reaches production.
What kind of data do we need to start an AI initiative?
Typical inputs include transactional and payments data, policy and claims records, customer profiles, KYC/AML information, logs from digital channels, and relevant external data (e.g., bureaus or market feeds). You don’t need a perfect data warehouse on day one, but you do need a clear view of where critical data sits, how it’s accessed, and which quality issues must be addressed for the first use case.
How do you integrate AI with our existing core systems?
We usually expose models and AI services via APIs or event‑driven architectures that connect to your core banking, policy administration, CRM, or case‑management platforms. Integration patterns are designed with your architecture and security teams, so model calls respect existing authentication, logging, and latency constraints instead of forcing a disruptive core replacement.
