Mastercard: AI Evolution Reshapes Finserv Landscape

The integration of generative artificial intelligence into financial services has entered a new phase, marked by practical implementation rather than speculation. 

This shift comes three years after the technology entered mainstream consciousness in late 2021, with organisations now focusing on commercial viability rather than technical possibilities. 

In Mastercard's latest signals report, Microsoft Founder Bill Gates characterises the technology as “the most important advance since the graphical user interface,” though OpenAI CEO Sam Altman admits he is “a little bit scared” of the technology his company has developed.

The financial sector's approach to generative AI implementation centres on three key developments: informed AI, which combines large language models with external data sources; perceptive AI, which interprets environmental data; and proactive AI, which operates with reduced human oversight.

Informed deployment

Banks and payment providers are implementing informed AI through two primary methods: fine-tuning and retrieval-augmented generation (RAG). Fine-tuning involves training an AI model on specific data sets, while RAG enables AI systems to access external databases in real-time.

The choice between these methods depends on data quality, update requirements and security considerations. 

This is particularly relevant in finance and law, where protecting proprietary information from unauthorised access is paramount. Current data indicates that 20% of enterprises employ fine-tuning, while 80% use RAG to supplement their language models.

In the banking sector, 73% of mortgage lenders view generative AI as central to improving operational efficiency in lending processes. 

The technology enables underwriters to incorporate market conditions and property trends into their analyses. Financial institutions are using the technology for fraud monitoring, transaction analysis and personalised financial advice.

Enterprise technology provider Cohere offers RAG capabilities tailored to business requirements, while data processing company Unstructured provides technology to convert unstructured data into RAG-compatible formats. 

Glean, a workplace technology provider, has developed an AI assistant that uses RAG to surface relevant information for financial professionals.

Informed deployment

Banks and payment providers are implementing informed AI through two primary methods: fine-tuning and retrieval-augmented generation (RAG). Fine-tuning involves training an AI model on specific data sets, while RAG enables AI systems to access external databases in real-time.

The choice between these methods depends on data quality, update requirements and security considerations. 

This is particularly relevant in finance and law, where protecting proprietary information from unauthorised access is paramount. Current data indicates that 20% of enterprises employ fine-tuning, while 80% use RAG to supplement their language models.

In the banking sector, 73% of mortgage lenders view generative AI as central to improving operational efficiency in lending processes. 

The technology enables underwriters to incorporate market conditions and property trends into their analyses. Financial institutions are using the technology for fraud monitoring, transaction analysis and personalised financial advice.

Enterprise technology provider Cohere offers RAG capabilities tailored to business requirements, while data processing company Unstructured provides technology to convert unstructured data into RAG-compatible formats. 

Glean, a workplace technology provider, has developed an AI assistant that uses RAG to surface relevant information for financial professionals.

Operational challenges 

The implementation of AI in financial services faces significant technical and regulatory hurdles. Banks must ensure AI systems operate on a zero-retention basis to protect sensitive information while navigating global regulations on data usage and processing. 

Poor data quality can lead to incorrect outputs while handling sensitive and proprietary data requires robust security protocols.

Financial institutions are exploring proactive AI systems, which can execute tasks through software interfaces with minimal human intervention. 

Currently, 10% of large companies use AI agents, with 50% planning implementation within 12 months. In automated financial planning, these systems can manage portfolios and optimise investments based on user goals and market conditions.

Start-up H, founded by former DeepMind scientists, is developing autonomous systems for financial operations. 

Adept AI, which received US$350m in funding in March 2023 from investors including General Catalyst and Spark Capital, creates solutions for software process automation. 

Imbue, backed by Amazon's Alexa Fund and former Google CEO Eric Schmidt, is developing models for autonomous financial tasks.

Industry applications

In fraud prevention, AI systems are becoming more sophisticated. Mastercard has integrated AI across its network, processing 143 billion transactions annually. 

The company's Decision Intelligence Pro solution analyses one trillion data points per year to detect fraudulent transactions, achieving fraud detection improvements of 20% on average, with some instances reaching 300%.

The technology is also transforming customer experience in financial services. Through its Dynamic Yield product, Mastercard delivers personalised experiences across digital channels, including web, mobile applications and digital assistants. In 2023, the system delivered 371 billion personalised impressions across 450 brands.

Banks are implementing AI-powered work assistants that can automate routine tasks like customer onboarding and report generation. These systems can retrieve documents and insights from corporate databases, streamlining processes that were previously manual and time-intensive.

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