Developing new products and services are central to remain relevant in the Banking Industry. Ignacio Bernal, Global Head of Architecture and Technology Transformation and Jose María Ruesta, Global Head of Infrastructure, Service & Open Systems, BBVA, share their journey towards a scale-out architecture which has enabled them to accelerate the cycle of innovation.
As analytics has become increasingly important for engaging with customers, Commonwealth Bank of Australia (CBA) shares how open source, open standards and hybrid cloud technologies are allowing the bank to more effectively manage, evolve and grow its analytics workloads while also reducing operational expenditures.
Mike Blalock, Intel GM of Financial Services discusses the progress of digitalization, the potential of AI, the need for banks to fix their data and their cultures to progress with both, and how technology can underpin banks’ transition to lifestyle companies.
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Having clocked up 377 million mobile transactions in Q1 2016, CaixaBank teamed up with Intel and Oracle to maximize its insights on this wealth of customer-generated big data. In the process of achieving its goals, it began to consider itself to be an information company, rather than a bank.
How does a 237-year-old bank introduce meaningful analytics? Should machine learning be allowed to pass judgement on customers? Can reassessing risk with big data protect both customers and banks from vulnerability? Some of the issues covered during a recent FSI panel discussion with Barclays at Fintech Connect Live in London.
Analytics shows its versatility in FSI with HSBC gaining insight from its 52 million customers to tackle banking fraud, while Aviva insurance invests in predictive analytics to maximize its targeting accuracy in personalized customer marketing.
Advances in technology have created an on-demand culture; consequently, there are increasing demands for innovative and secure online, mobile, and wearable technology-driven solutions.
To compete in a world where technology plays an ever-increasing role in the daily lives of customers, financial services institutions must embrace change like never before. Finding new ways to service customers, while continuing to meet compliance and regulatory demands, is going to require a fundamental reshaping of the traditional business model in financial services. This is going to require the kind of agility that only comes with a cloud architecture approach, whether private, public or a multi-cloud strategy.
As an early adopter of an Intel® technology-based intelligent, artificial neural-network risk-control system, China UnionPay has demonstrated the value of using machine learning to drive proactive, highly efficient, and accurate risk identification and mitigation workflows.
NASDAQ deals with millions of transactions every second. Data is fundamental to their business, but implementing a typical big data project is difficult to manage. See how NASDAQ overcame these challenges through implementing BlueData software.
Intel IT used design best practices to create a cloud application platform (Paas) which makes it easy to create, deploy, and manage web and mobile applications.
The amount of existing and new data flowing through today’s financial organizations is enormous and growing. The untapped potential of that data is even greater. By gathering up and analyzing the many varieties of data, businesses and organizations discover insights previously hidden. The application of machine learning and artificial intelligence solutions from Intel will provide deeper insight on customer behaviors and preferences, which can unlock new business opportunities, transform business models, and deliver new classes of services. This will also serve to meet the demands of legislation and, crucially, help to combat cyber-crime.
Nervana’s deep learning platform delivers advanced analytics helping companies prevent fraud and enhance customer service.
The data-driven financial services industry possesses a vast amount of information about its clients and thus bears significant responsibilities with regulations and compliance. Growing risk and regulatory compliance pressures can be served effectively and efficiently with a centralized approach to data management and analytics.
Read up on these three customer case studies, all examining how central, open, and scalable enterprise data hubs can be the solution engines for the financial services industry.
Respond to growing risk management and regulatory compliance pressures and costs with a centralized approach to data management and analytics.
Security breeds trust, and trust secures customer loyalty – an imperative for business today. Banks need to ensure they are providing secure, relevant, and cutting edge customer-centric experiences that can mitigate threats from cyber-crime and fraud, as well as solutions that protect their IT infrastructure.
Help combat cybercrime with Intel®-based end-to-end cyber security solution that utilizes a powerful data aggregation tool and machine learning to predict threats.
Transforming technology in the workplace can have a hugely positive impact on employees and improve business efficiencies, enabling collaboration and securing data in the workplace. Equally these changes can help improve front-end customer experiences and ensure solutions are aligned to the overarching business strategy.
Cut meeting delays and create a collaborative environment for in-room and virtual teams.
Shape your work environment around the demands of your job, transitioning from mobile to deskbound work seamlessly.
Improve service quality, reduce operation costs, and safeguard the enterprise by enabling a consistent policy-driven approach.