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The banking industry is changing rapidly as institutions aim to be more effective and intelligent. Trends like rising cybersecurity risks, mobile banking, and digital payments combined with exponential data growth are challenging bank data management and performance. With so much intelligence to utilize and secure, institutions must find ways to quickly convert their data into insights, all at unprecedented speed and scale.

Data scientists are feeling the strain of these trends as they gather and interpret large sets of structured and unstructured data types. Without the right resources, data scientists have to work harder to collect, analyze, and model data—from diverse sources like transactions, customer information, customer engagements, daily operations, and workflows—then turn their findings into actionable plans. Banks require these critical inputs to develop best practices, from value and risk modeling to predictive analytics.

A survey of 1,440 banks found that fast-growing institutions are more likely to invest in technology that supports data initiatives versus other types of technology. These institutions make ongoing investments to expand data usage and drive business growth. Still, many banks need the infrastructure to manage the flood of information. Legacy environments lack the compute, speed, and flexibility to execute intensive data and analytics workloads. To better utilize their data, banks are searching for next-generation solutions to help them efficiently allocate resources, improve performance, and make smarter decisions.

Gaining insight on demand with AI

Banks are leveraging artificial intelligence (AI)capabilities to transform their operations. AI is the foundation for faster and more informed banking, enabling institutions to optimize, accelerate, and secure their operations. Banks that invest in AI technologiesare accelerating time-to-value in four main applications—fraud detection and identity verification, conversational AI and speech recognition, robotics process automation (RPA) for document processing, and recommendation engines.

Risk management is a chief concern in banking—an initiative spanning security, regulatory compliance, fraud, anti-money laundering (AML), and know your customer (KYC) guidelines. With cyberattacks and other criminal activity on the rise, fraud detection and identity verification are crucial to mitigate as well as prevent breaches. These applications perform real-time calculations of risk and fraudulent exposure by processing countless transactions, payments, and customer details. Banks can flag anomalies in milliseconds and take immediate action to solve problems and heighten data security.

Conversational AI and automatic speech recognition use natural language inputs to enhance customer support experiences. Instead of taking call notes and feeding information into the system, banks transcribe events in the call center in real-time. The applications analyze video and audio content instantly to gain insights from customer engagements and streamline daily processes.

Read more here:

https://community.hpe.com/t5/Tech-Insights/Achieve-banking-insights-on-demand-with-AI/ba-p/7134701?ncid=so-link-389732-vt09&linkId=100000045506805#!&cid=ix06_so-link_en-us

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