Real-world AI application
Abstract
Artificial Intelligence (AI) technologies have been adopted to ensure that machines can perform cognitive functions without humans’ direct intervention, such as creativity, problem-solving, reasoning, perception, and interaction with the environment. The banking and financial sector operations are sensitive and need intensive cognitive functioning, thus incline an organization to incorporate AI-based systems in the organizational operations.
Introduction
The banking and financial services have consistently adopted AI-based systems to handle various sensitive financial operations and activities. Financial and banking operations are faced with a wide range of threats as they handle sensitive and delicate operations and functions that involve the exchange of funds, sensitive data, insurance services, banking and asset management (Mekinjic, 2019). The adoption of AI-based systems is thus incorporated in the banking and financial services industry operations to enhance customer support, detecting credit card fraud and other anomalies. Therefore, the AI system presents an effective preventive measure to threats and risks attached to financial services and transactions, thus upholding the safety and security in the banking and financial sectors.
Background
The application of AI intelligence in the banking sectors is a vital aspect of cushioning the bank from losses associated with risk and threats to financial operations and services. Additionally, the AI-based systems are tasked with improving customer services to ensure that customers are sufficiently and effectively attended (Sbarcea, 2019). The banking and financial services need to have surveillance and management at all times without the help of man, and thus the use of AI systems has effectively ensured that financial services are handled effectively and efficiently in the interest of the customers and banking businesses.
The banking and financial sectors have been using AI for a considerable period, but the use was only limited to specialized applications. In recent times, the AI solution has been incorporated on a wide range of industry applications. Banks have increased their investment in research and development of AI applications and technologies to ensure the safety of operations, improve customer experience, manage compliance, and increase data processing capability (Menacho and Martin, 2019). Moreover, AI technology in the banking and financial sector has its share of opportunities and challenges. On the challenges, ethical considerations, the AI incorporation compromises the confidentiality and integrity of customer data to the insiders in the name of safeguarding it. On the opportunities, the system has the capability of enhancing customer interaction and operations, achieving a high level of efficiency in the banking process and enhancing security and risk controls.
Methodology
The methodology takes the application of AI in the context of different financial services offered. The methodology in applying AI encompasses a set of principles, establishment of problems, algorithms, the procedure of extracting the non-obvious, used of defined patterns and actionable insight from the large data set. The imp0lmentation process follows the aspect of gaining sense, reason, learn and take action. Data science is vital in the implementation of AI systems. Data science takes the machine learning process and data mining (Marsh, Oliver and Bryan, 2019). Machine learning takes the design and Assessment of algorithms for extracting patterns of data. On the other hand, data mining takes the analysis of structured data. Moreover, data science takes the handling of challenges in financial services by capturing, cleaning, and transforming unstructured data, adopting big data technologies, using big data technologies, and processing.
Sense Reason Learn Take action
– Computer vision and gesture control
– Natural language processing
– Cognitive computing
-Smart analytic/ processing Machine Learning
-Large-scale machine learning
– Supervised learning and enforcement learning
– Supervised learning and reinforcement learning -Dialogue interface
-Virtual Helpant interface
-Robotic
Table 1
The methodology in implementing the AI system is defined through a consistent process of gaining sense to the operations, reasoning based on the available data, learning about the data to enhance actions.
Discussion
The adoption of an AI-based system in the banking sector takes the digitization of operation driven by automation of the process to gain effectiveness and cost-efficiency. Digitization is long and colossal that involves a combination of numerous processes (Latimore, 2018). The process involved includes standardization of data input configurations, data is categorized based on their types and the management of historical and legacy data and harmonization of data standards.
The digitization and (AI) harness big data concerning people and their different behaviors. The data is analyzed and sourced from three categories of “known unknowns,” known as unknown and unknown unknowns (Latimore, 2018). The “known knowns” entails the data that exists, and it is known, such as the bank account and personally identifiable data of the customers. The “known unknowns” entails data arising from people’s activities such as health data from fitness wearable and digital health apps or data from online shopping. The “Unknown unknowns” entails data created by the banks without its knowledge, such as the metadata creates using eyeball tracking, facial recognition and gesture tracking in using mobile devices. Customer identities can be identified when powerful and aggregated analytics.
The AI application can handle and manage various cybersecurity issues. In this regard, AI-based systems in the bank are responsible for securing the aspect of digital and data resilience and transformation (Latimore, 2018). Data stored, transmitted, or being processed have its confidentiality and integrity upheld to protect the data owners. The concerned organizations need to ensure that their systems are consistently improved through innovations and development for effective protection and security.
It is vital to note that the cybersecurity in the banking and financial services industry is prone to be disrupted due to advances in adversarial threats capable of leveraging AI, thus making protection impossible(Latimore, 2018). For instance, the use of malware attacks or fraud-as-a-service models makes it possible for cybercriminals to compromise the banking systems. In this regard, protective and preventive measures need to be consistently improved and adopted in the organization’s operations.
Conclusion
Banking and financial services industries face a wide range of challenges in the course of offering different services, and the challenges can be addressed through the incorporation of AI-based systems in the operations. The banking and financial industries’ challenges entail the risks and threats exposed to sensitive data and operations.
AI-based systems in the banking sectors have a wide range of uses directed towards achieving great efficiency and effectiveness. The AI enhances customer interactions, banking operations and security purposes. In the interaction of the customers, there is the Robo-advice and taking and addressing customer complaints. Robo-advice ensures that customers get algorithm-driven financial and investment management advice. On customer complaints, the technology ensures that customer queries are addressed in time. Consequently, on banking operations such as credit scoring that uses statistical modeling in the financial sector. More so, on security purposes, the system ensures that fraud is effectively prevented. The system detects fraud and suspicious activities related to financial crimes.
The AI system needs to be implemented in the banking and financial sector to streamline operations in the interest of all the parties involved. Different risks and threats affect the operation in the banking sector, but the AI technologies standardize the operations for efficiency and effectiveness.
References
Latimore D. (September 2018). ARTIFICIAL INTELLIGENCE IN BANKING. WHERE TO START? Celent Journal 11-22.
March, Wyman O. and Bryan C. (2019). ARTIFICIAL INTELLIGENCE APPLICATIONS IN FINANCIAL SERVICES. ASSET MANAGEMENT,BANKING AND INSURANCE.
Mekinjić, B. (2019). The impact of industry 4.0 on the transformation of the banking sector. Journal of contemporary economics, 1(1).
Menacho, V. S. J., & Martin, A. (2019). Cyber Governance and the Financial Services Sector: The Role of Public-Private Partnerships.
Sbarcea, I. R. (2019). Banks Digitalization-A Challenge for the Romanian Banking Sector. Studies in Business and Economics, 14(1), 221-230.
