9 min Read<\/p>\n
97 percent of German financial institutions already use artificial intelligence. Only three percent operate entirely without AI. But what lies behind these figures? In many cases, AI deployment is limited to chatbots in customer service and basic automations. The truly transformative applications – AI-driven credit decisions, real-time fraud detection, personalized investment advice – are still in the pilot phase for most. Germany leads the world in core banking modernization with AI. But this lead is fragile.<\/strong><\/p>\n The PwC study on AI usage in the German financial sector (January 2025) delivers a sobering insight: almost everyone is doing something with AI, but very few are fully exploiting its potential. The most common use case is customer service – chatbots that answer standard questions. The second most common: internal process automation such as document classification and compliance checks.<\/p>\n The truly valuable applications – real-time fraud detection<\/a>, AI-powered credit scoring models, personalized investment recommendations – are still in the experimental phase for most institutions. The reason is not technical, but regulatory: the EU AI Act classifies AI in credit granting as high-risk. This means obligations for explainability, documentation, and human oversight.<\/p>\n Nevertheless, there are institutions that are leading the way. Deutsche Bank has invested in the AI startup Aleph Alpha and is building its own large language models for internal processes. ING is using AI for automating Know-Your-Customer checks. And several Volksbanken are testing AI-powered advisory tools that provide customer advisors with real-time data.<\/p>\n Germany is not renowned as a FinTech pioneer. However, when it comes to modernizing core banking systems, the country leads the world with 34 percent of projects, according to PwC. This might seem paradoxical, but it isn’t. German banks operate on legacy systems that are sometimes decades old and extremely costly to maintain. The pressure to change is higher in Germany than in markets that transitioned to cloud-native architectures earlier.<\/p>\n The modernization process is unfolding in three waves. First, migrating mainframe systems to cloud platforms. Second, integrating AI modules for real-time analytics – transaction monitoring, liquidity management, and regulatory reporting. Third, building data platforms that supply AI models with clean, structured data. The third wave is the most challenging because it requires breaking down data silos that have grown over decades.<\/p>\n “The shift from ‘AI as an add-on’ to ‘AI as an architectural principle’ is the crucial paradigm shift by 2026. Banks that do not make their core systems AI-ready will lose not just efficiency but also the ability to keep up with regulatory demands.”<\/p>\n PwC Financial Services Technology Report, 2025<\/p>\n<\/div>\n 45 percent of German financial institutions already use AI assistants and chatbots in lending. However, their use is currently limited to support functions: training employees, detecting errors in applications, and checking documents for completeness. The actual credit decision is still made by a human.<\/p>\n This is due to regulatory reasons. The EU AI Act classifies AI systems that assess creditworthiness or make credit decisions as high-risk applications. This means: mandatory risk assessments, transparency towards affected parties, human oversight, and regular audits. Many banks shy away from the effort and stick with human decision-makers supported by AI.<\/p>\n Smarter institutions are strategically using the transition period. They are building AI models that recommend credit decisions, collecting data on their accuracy, and documenting the entire process so that it complies with the AI Act. Once regulatory practices become clearer, they can scale up quickly.<\/p>\n A Forrester forecast is causing concern: Direct web traffic to financial services websites will decrease by 20 percent in 2026. The reason: AI agents and AI search engines<\/a> provide users with direct answers without requiring them to click on a bank’s website.<\/p>\n For banks, this means that if they do not appear as a source on ChatGPT, Perplexity, or Google AI Overviews, they will lose visibility among potential customers. The solution is not less content, but better content. Structured data, FAQ sections, clear facts with citations – these are the signals that AI systems recognize as worthy of citation.<\/p>\n The next big step is personalization. 36 percent of German financial institutions are already investing in personalized bonus and reward models based on AI analyses. The trend is shifting from rule-based systems (“Customer has X euros in sales, receives Y percent discount”) to predictive models (“This customer has a 78 percent chance of canceling in three months, so proactive offer”).<\/p>\n At the same time, pressure is mounting due to regulatory AI requirements. DORA, AI Act, GDPR – financial institutions must innovate and ensure regulatory compliance simultaneously. Those that see this as an integrated task rather than a contradiction will gain the most from AI.<\/p>\n The most common applications are chatbots in customer service, document classification, compliance automation, and fraud detection. AI-driven credit decisions are predominantly still in the pilot phase.<\/p>\n<\/details>\n In principle, yes, but under strict conditions. The EU AI Act classifies credit scoring as high-risk AI. This requires transparency, human oversight, regular audits, and the ability for affected parties to challenge decisions.<\/p>\n<\/details>\n Because the pressure to act is particularly high. Many German banks operate on decades-old mainframe systems with high operating costs. The combination of cost pressure, regulatory requirements, and the potential of AI is driving modernization.<\/p>\n<\/details>\n Forrester projects a 20 percent decrease in direct web traffic to financial websites by the end of 2026. Banks need to adapt their digital strategies: structured content, FAQ sections, and machine-readable data will become more important than classic SEO optimization.<\/p>\n<\/details>\n The AI Act provides legal certainty but increases compliance efforts. Credit scoring and insurance assessments are considered high-risk AI. Banks must implement risk management systems, make decisions explainable, and conduct regular audits.<\/p>\n<\/details>\n Source header image: Pexels \/ Kindel Media<\/em><\/p>\n","protected":false},"excerpt":{"rendered":" Nearly all German financial institutions use AI. Transformative applications are in the pilot phase.<\/p>\n","protected":false},"author":160,"featured_media":89070,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_yoast_wpseo_focuskw":"AI in banking","_yoast_wpseo_title":"AI in banking: What banks are using it for now","_yoast_wpseo_metadesc":"Nearly all German financial institutions use AI. Transformative applications are in the pilot phase.","_yoast_wpseo_meta-robots-noindex":"","_yoast_wpseo_meta-robots-nofollow":"","_yoast_wpseo_meta-robots-adv":"","_yoast_wpseo_canonical":"","_yoast_wpseo_opengraph-title":"","_yoast_wpseo_opengraph-description":"","_yoast_wpseo_opengraph-image":"","_yoast_wpseo_opengraph-image-id":0,"_yoast_wpseo_twitter-title":"","_yoast_wpseo_twitter-description":"","_yoast_wpseo_twitter-image":"","_yoast_wpseo_twitter-image-id":0,"featured_post_sortierung":0,"featured_post":0,"pre_headline":"","bildquelle":"","teasertext":"","language":"de","_evm_translation_lang":"","_wp_old_slug":[],"footnotes":""},"categories":[2212],"tags":[],"class_list":["post-92664","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fintech-financial-services","entry"],"evm_reading_time_minutes":7,"wpml_language":"en","wpml_translation_of":89071,"acf":[],"yoast_head":"\nKey Takeaways<\/h2>\n
\n
The Current State: Much AI, Little Depth<\/h2>\n
PwC Study, January 2025<\/span><\/div>\n<\/div>\n
highest value worldwide (PwC)<\/span><\/div>\n<\/div>\n
Forrester forecast 2026<\/span><\/div>\n<\/div>\n<\/div>\nCore Banking Modernization: Germany’s Unexpected Lead<\/h2>\n
AI in Lending: Opportunity and Regulatory Grey Area<\/h2>\n
The Invisible Shift: AI Search Eats Away at Bank Websites<\/h2>\n
What’s Next<\/h2>\n
Frequently Asked Questions<\/h2>\n
Which AI applications do German banks use most frequently?<\/summary>\n
Can AI make credit decisions in Germany?<\/summary>\n
Why is Germany leading in core banking modernization?<\/summary>\n
What does the projected decline in traffic mean for banks?<\/summary>\n
How does the AI Act impact AI in the banking sector?<\/summary>\n
Further Reading<\/h2>\n
\n