44% of work is automatable in Financial Services - McKinsey & Company
The Four Pillars of the Fully Automated Enterprise for Banking
Let AI tackle your tasks, freeing you up for endless innovation
AI
Robot assistants amplify human productivity and potential
RPA
Delegate repetitive tasks to robots for streamlined and efficient automation.
Empower every team member with their personal robot assistant for seamless productivity and efficiency across the company
Empower everyone to develop
Transform your tasks effortlessly with a user-friendly app that automates work with ease, making every job simpler and more efficient.
Unleash AI across every aspect of your work
Unlock the power of AI to craft intelligent robots that effortlessly identify and seize every opportunity for automation, ushering in a new era of streamlined efficiency and innovation
Augment
Diversify
AI-driven chatbots provide 24/7 customer support
Banks are expanding their use of AI technologies to improve customer experiences and back-office processes
Front Office
Back Office
Smile-to-pay facial scanning to initiate transaction
Conversational bots for basic servicing requests
Micro-expression analysis with virtual loan officer
Humanoid robots in branches to serve customers
Biometrics (voice, video,print) to authenticate andauthorize
Machine vision and natural language processing to scan and process documents
Micro-expression analysis with virtual loan officer
Real-time transaction analysis for risk monitoring
78% of companies surveyed by Deloitte have or are implementing RPA; 16% plan to do it shortly
Use Cases:
Enhanced Data Security
AI-powered security solutions detect and respond to cybersecurity threats in real-time, safeguarding sensitive financial information and preventing data breaches. By leveraging AI for threat detection, anomaly detection, and behavioral analysis, banks can enhance their cybersecurity posture and protect against evolving cyber threats.
CHATBOT
AI-powered chatbots provide immediate assistance to customers, answering inquiries, resolving issues, and guiding users through various banking processes. By leveraging natural language processing (NLP) and machine learning, chatbots deliver human-like interactions, improving the overall customer experience while reducing the burden on human agents.
Customer
AI algorithms analyze vast amounts of customer data to understand individual preferences, behaviors, and needs. Banks can use this insight to offer personalized product recommendations, targeted marketing campaigns, and customized services, enhancing customer satisfaction and loyalty.
Data Management
RPA can automate data extraction, validation, and reconciliation processes, enabling banks to efficiently manage large volumes of data from disparate sources. By leveraging RPA for data analysis and reporting, banks can gain valuable insights into customer behavior, market trends, and operational performance, empowering data-driven decision-making.
Use Cases:
Enhanced Data Security
AI-powered security solutions detect and respond to cybersecurity threats in real-time, safeguarding sensitive financial information and preventing data breaches. By leveraging AI for threat detection, anomaly detection, and behavioral analysis, banks can enhance their cybersecurity posture and protect against evolving cyber threats.
CHATBOT
AI-powered chatbots provide immediate assistance to customers, answering inquiries, resolving issues, and guiding users through various banking processes. By leveraging natural language processing (NLP) and machine learning, chatbots deliver human-like interactions, improving the overall customer experience while reducing the burden on human agents.
Customer
AI algorithms analyze vast amounts of customer data to understand individual preferences, behaviors, and needs. Banks can use this insight to offer personalized product recommendations, targeted marketing campaigns, and customized services, enhancing customer satisfaction and loyalty.
Data Management
RPA can automate data extraction, validation, and reconciliation processes, enabling banks to efficiently manage large volumes of data from disparate sources. By leveraging RPA for data analysis and reporting, banks can gain valuable insights into customer behavior, market trends, and operational performance, empowering data-driven decision-making.