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.
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 Platform 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®.
- Customer Story Using Artificial Intelligence to better engage with customers Daiwa Securities uses analytics and machine learning from SAS to better meet customer needs
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AI Solutions for Banking
- SAS® Anti-Money LaunderingTake a risk-based approach to monitoring transactions for money laundering and terrorist financing activities.
- SAS® Credit ScoringDevelop, validate and monitor credit scorecards faster, cheaper and more flexibly than any outsourcing alternative.
- SAS® Data PreparationQuickly prepare data for analytics in a self-service, point-and-click environment with data preparation from SAS.
- SAS® Fraud ManagementDetect, prevent and manage fraud enterprisewide in real time – from a single platform.
- SAS® Intelligent DecisioningEnable analytically driven real-time customer interactions, and automate operational business decisions 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® Regulatory Risk ManagementProactively manage regulatory risk across multiple jurisdictions with a single, end-to-end risk management environment.
- SAS® Visual Data Mining and Machine LearningSolve your most complex problems faster with a single, integrated in-memory environment.
- SAS® Visual Text AnalyticsUncover insights hidden in text data with the combined power of natural language processing, machine learning and linguistic rules.