Due to high volume, accuracy and quantitative nature, the sector of finance and insurance can derive huge value from AI. AI and machine learning applications can help finance and insurance professionals with everything from loan approval and assets management to risk assessment.

As computing power and machine learning tools have become more accessible, the ways of how it can be used within both finance and insurance are growing exponentially. AI and ML deeply change the functioning of this sector in areas including both products, processes, and analytics, solving specific problems in customer engagement, financial management and compliance.

By adopting and applying AI in finance and insurance, managers can take data-driven management decisions by “asking” algorithms and machine learning models questions that are pertinent to their business.

AI is being used actively in the financial sector today.

Examples of AI application in Finance

  • Fraud detection: Previously, when detecting fraud in the financial world, the system relied almost exclusively on a set of complex rules. Today, fraud detection systems exceed a checklist of risk factors. Instead, it can learn and calibrate to both potential or real threats of security.
  • Loan underwriting: Machine learning algorithms can be trained on a vast amount of variables within consumer data and financial lending or insurance results. Assessing trends within consumer behavior with algorithms can help companies predict trends that might have an influence on loans and insurance in the future.
  • Portfolio management: Algorithms can be built to calibrate financial portfolios to the preferences and risk appetite of the user, integrating real-time changes in the market.

Use cases

Creation of a model that analyzes past client lifecycles and the likelihood of churn.
Settlement prediction
Predicting whether a settlement will be effectuated before a given deadline or not, based on actual and historical settlements
Client reaction based on scenarios in Finance
A supervised model that predicts which client might react in a different market scenario.
Payment matching
Automating matching of incoming and outgoing payments - and presenting the processor with intelligent choices for "best fit" matches
Payback of account payable
A supervised learning approach is used to predict from new customers whether they will be able to pay their accounts payable on time
Message categorization
Classification of messages, emails or documents for human or automated processing
Credit scoring
Using machine learning algorithms to take actual historical data to determine creditworthiness and repayment ability
Market data cleaning
Detecting anomalies (e.g. missing values) and then assigning abnormal values an averaged/fitting value

Meet the industry leader

Christian Villumsen

Christian Villumsen

Finance & Insurance, 2021.AI

Christian is a passionate Senior Advisor with 20 years of experience within the Fintech market. He has a proven track record in Fintech, Credit & Market Risk, Compliance, Project, Program, and Portfolio Management. Previously, he worked as a Director at Saxo Bank and spearheaded the Global Enterprise Risk initiative. in

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