While the AI market value of nearly 100 billion will skyrocket twentyfold by 2030, up to 2 trillion, the FinTech market, currently valued at 190 billion, will grow 20% year by year and exceed 500 billion.
In this growth, AI augmented automation is an effective tool for solving financial operations problems, improving efficiency and reducing manual errors. AI augmented automation can resolve diverse operational tasks, from onboarding to in-life management and exit, providing the expected customer experience while ensuring the required control. AI augmented automation can streamline workflows, enhance decision-making, and improve performance.
What is AI augumneted automation?
AI augmented automation is a modular design of software technologies and human actions meant to address complex requirements and processes within day-to-day operations.
Here is an example: a customer opens a new bank account by uploading a selfie, a copy of his ID and a utility bill during an online session.
The automation will extract the relevant data points from the documents using digitisation, match the face picture to the ID using machine learning, and validate the request. The decision engine will then decide the appetite and inform the journey. Upon customer acceptance, it will streamline data to internal systems and instruct human validation, ensuring a seamless customer experience and efficient compliance.
AI augmented automation components
AI augmented automation must have access by design to various components to satisfy complex financial operation needs derived from constantly changing regulations and customer necessities.
Most components are cloud-based services, so that cloud-ready organisations can benefit from easy integration and rapid implementation. At the same time, hybrid or local solutions can leverage required components using containerisation techniques.
AI augmented automation components list:
Machine Learning enables the AI to learn and adapt to new data inputs over time, automate decision-making and improve the accuracy of predictions.
Natural Language Processing allows AI to understand and interpret human language and automate customer service tasks.
Computer Vision enables machine interpretation and understanding of visual information and can resolve tasks like quality control or object recognition.
RPA-AI combined can automate complex tasks that require cognitive skills, such as problem-solving or decision-making.
AI Workflows simplify routine tasks such as data entry, processing, analysis and reporting.
Intelligent Agents or AI-powered software applications that can interact with humans and automate tasks like customer inquiries or meeting schedulers.
Knowledge Management captures and stores knowledge, which AI can use to improve performance over time and automate tasks like data entry or document classification.
Cognitive Search to efficiently identify and explore relevant content at scale.
AI augmented automation design
An AI augmented automation system designed under modular design principles with consideration given to multiple inputs, decision engines, and data streaming can provide organisations with a powerful tool for optimising financial processes and making data-driven decisions.
Different modules developed for specific tasks can process and analyse multiple inputs, such as natural language processing for text or computer vision for images. The decision engines are standalone modules that explore the information and make recommendations based on predefined rules and machine learning models. Finally, data streaming modules collect and process real-time data, allowing the system to decide based on the most up-to-date information.
A modular design approach allows every component to develop and update separately, enabling organisations to adapt quickly to changes.
Considerations when designing an AI augmented automation system:
User Interface design makes it easy for users to interact with the system by displaying relevant information, providing easy-to-use controls, and minimising the number of clicks required to complete a task.
Data Management design handles large amounts of data efficiently and provides high performance under heavy loads and scalability as the system grows.
Integration design ensures the efficient sending and retrieval of data to and from other systems within the organisation's ecosystem. Various protocols, APIs and connectors communicate with other systems in real-time.
Automation design balances repetitive, rules-based tasks that do not require human intervention with augmented activities where human input is needed. The design will recognise patterns and automate tasks based on those patterns, ensuring that the system can handle exceptions or errors and monitor activities.
The ML design leverages algorithms to make predictions and recommendations based on data. The system will collect and store data, create models for machine learning algorithms, and ensure continuous learning.
The system design will ensure high performance under heavy loads and scalability as the system grows. It includes handling large volumes of data, ensuring optimal performance, and providing tools for monitoring and managing the system's performance.
The system design will ensure the security and privacy of data and information, including encrypting data at rest and in transit, secure access, and tools for monitoring and managing security and privacy risks.
The right balance
It is essential to emphasise the balance between humans and technology when designing an AI-augmented automation system. While automation and AI can improve efficiency and accuracy, it is crucial to recognise that human expertise and decision-making remain essential.
Therefore, the design should ensure that humans remain at the centre of the decision-making process and that the system enhances human capabilities rather than replace them.
It is also important to consider the environmental impact of an AI augmented automation system. Optimising financial processes and reducing waste contribute to a more sustainable future. However, ensuring that the system design addresses energy efficiency and responsible use of resources is also essential. Considering the system's environmental impact during the design phase is the only way to ensure that the benefits of an AI-augmented automation system are not at the expense of our planet's health and sustainability.