Industry Applications
AI for Banking
Meet customer and regulatory compliance demands with greater speed, accuracy, efficiency and cost-effectiveness.
Business Challenges
Changing regulatory compliance requirements and shifting customer demands mean a bank’s survival hinges on its ability to glean relevant insight from all available data. In fact, the efficient and effective use of data is critical to addressing many issues today's banks face – combating fraud and financial crimes, managing credit and regulatory risk, enhancing the customer experience and generating sufficient capital. A partnership between humans and machines – each augmenting the other – holds the most promise for successfully achieving compliance and meeting customer needs, but knowing where and how to start isn't always easy.
How AI Can Help
From fraud to credit to risk to customer experience, artificial intelligence (AI) can enhance the speed, precision and effectiveness of human efforts, which results in a more responsive, more profitable bank. With AI capabilities from SAS, you can:
- Turn customer experience into customer engagement. With embedded AI tools, you can stitch data together from all sources, providing an accurate and evolving view of the customer journey. You can then optimize customer journeys across all channels to maximize engagement and enable real-time decisioning.
- Quickly identify fraudulent transactions. Use AI and machine learning techniques to identify which types of banking transactions are likely to be fraudulent. AI techniques, including adaptive machine learning and unsupervised intelligent agents, can predict fraudulent transactions in real time – and reduce false positives – based on changes and inconsistencies in customer behavior patterns. Reducing false positives boosts customer satisfaction, protects revenue and lowers costs.
- Adopt fast, accurate credit scoring policies. When a potential customer applies for a loan or credit card, use AI and machine learning techniques to analyze alternative data sources – like utility payments, mobile phone use and text message activity – for improved loan rating accuracy to give good customers faster access to credit using real-time decisioning.
Why choose SAS?
As the leader in advanced analytics, SAS advocates applying analytics to any data that has the potential to produce insights. That's why we embedded AI capabilities in our software – from the powerful SAS Viya to solutions tailored to the needs of the banking industry. SAS delivers open, trusted, scalable and sustainable AI capabilities that can helps banks of all sizes achieve growth, profitability and compliance. For more than 40 years, SAS has delivered consistent value to the banking industry, and more than 3,500 financial institutions around the world choose SAS to gain THE POWER TO KNOW®.
Recommended Resources
AI Solutions for Banking
- SAS® Anti-Money LaunderingVerfolgen Sie einen risikobasierten Ansatz bei der Überwachung von Transaktionen auf Geldwäsche und Terrorismusfinanzierung.
- SAS® Credit ScoringDevelop, validate and monitor credit scorecards faster, cheaper and more flexibly than any outsourcing alternative.
- SAS® for Data Preparation and Data QualityHarness the power of clean, reliable data.
- SAS® Fraud ManagementErkennen, verhindern und verwalten Sie Betrug unternehmensweit in Echtzeit - von einer einzigen Plattform aus.
- SAS® Intelligent DecisioningEasily create, manage and govern robust, analytically driven business rules to power decisioning at scale.
- SAS® Model Risk ManagementSignificantly reduce your model risk, improve your decision making and financial performance, and meet regulatory demands with comprehensive model risk management.
- SAS® Solution for Regulatory CapitalProactively manage regulatory risk with a single, end-to-end risk management environment.
- SAS® Visual Data Mining and Machine LearningSupport end-to-end data mining and machine learning processes with comprehensive visual and programming interfaces for users of all skill levels.
- SAS® Visual Text AnalyticsScale the human act of reading, organizing and extracting useful information from huge volumes of textual data.