Artificial Intelligence in Banking
Nowadays, Artificial Intelligence (AI) demonstrates some unique opportunities to increase prosperity and growth. For the banking sector, it provides great opportunities to develop customer experience, democratize financial services, enhance cyber-security and consumer protection, and manage risks properly. This essay aims to discuss some aspects and reasons for using Artificial Intelligence as an advanced issue in the banking sector.
Introduction and Overview
Basically, defining artificial intelligence (AI) would not be an easy task. This means the major is exceedingly broad, which it cannot be limited to a specific area of research. AI seeks to understand how human cognition works by creating cognitive processes that emulate those of human beings. AI is at the crossroad of multiple majors such as computer science, mathematics (logic, optimization, analysis, probabilities, linear algebra), and cognitive science. In addition, these core scientific disciplines require to be mixed with the specific knowledge of the majors they are applied to, and each algorithm in AI is provided by a combination of techniques and methods such as semantic analysis, symbolic computing, machine learning, exploratory analysis, deep learning, and neural networks.
Artificial Intelligence (AI) is defined as “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.
Marvin Lee Minsky, who is considered as one of the founding fathers of AI, defines it as follows: “the science of making machines do things that would require intelligence if done by men. It requires high-level mental processes such as: perceptual learning, memory and critical thinking.” In other words, artificial intelligence is the science of building computer programs that aims to perform tasks that would need some intelligence if they were done by human beings. As a result, human activities seem to completely covered such as moving from one place to another, learning, reasoning, socializing, and creativity. However, we are still far from creating a machine that would be able to match or outperform.
Artificial Intelligence is the theory and development of computer systems which are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation among languages.
AI includes some principles such as problem definitions, algorithms, and processes for extracting non-obvious, useful patterns and actionable insight from large data sets. The term of data science is closely connected to “Machine Learning” as well as “Data Mining”. Machine learning (ML) focuses on the design and evaluation of algorithms for extracting patterns from data, and Data Mining generally copes with the analysis of structured data. Data science; on the other hand, also takes into account other challenges such as the capturing, cleaning, and transforming of unstructured data, the use of big data technologies to store and process big, unstructured data sets, as well as questions related to data ethics and regulation.
Machine Learning (ML) is the study of computer algorithms that enhance automatically through experience. It is seen as a subset of Artificial Intelligence (AI). Machine learning algorithms build a mathematical model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do.
Data mining is a process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
Additionally, Artificial Intelligence is used in lots of FinTech solutions. It would be a great solution for the some challenges faced by many businesses such as customer experience, personalization, loyalty building, and fraud detection. In the early 90s, AI and Machine Learning (ML) appeared on Wall Street along with the first hedge funds; however, there was still no significant breakthrough. It appeared only with the increased availability of data, generally with the spread of the Internet. Since then, there has been an extremely quick evolution of Operating Systems, taking advantage of the growing capabilities of machines. Recently, AI affects every area of a bank’s operations as well as the work of departments that we often forget about in the context of using technology in the financial sector, such as corporate core aspects, including even human resource team work.
Use cases
Fundamentally, there are some use cases for using AI in banking for developing and improving the process and activities. The use cases are classified in three categories. These topics can highlight the potential areas of opportunities for the banking sector.
- Improving customer interaction and experience. For example, chat-bots, voice banking, robo-advice, customer service improvement, bio-metric authentication and authorization, customer segmentation (e.g., by customized website to ensure that most relevant offer is showed), targeted customer offers.
2. Enhancing the efficiency of banking processes and operations. For instance, process automation or optimization, reporting, predictive maintenance in IT, complaints management, document classification, automated data extraction, Know-Your Customer document processing, credit scoring, etc.
3. Improving security purposes and risk control. For instance, enhanced risk control, compliance monitoring, different types of anomaly detection, Anti-Money Laundering detection and monitoring, system capacity limit prediction, support of data quality assurance, fraud prevention, payment transaction monitoring, cyber risk prevention.
Robo-advice: AI for customer Interaction (an example)
As a matter of fact, Robo-advisors are automated platforms that support algorithm-driven financial and investment management advice. This start from the information collected from individuals, and uses a mixture of various technologies such as cognitive systems, machine-learning, natural language processing, expert systems, and artificial intelligence algorithms. The robo-advisor can be able to suggest, automatically or with a financial advisor’s support, possible investment solutions, tailored to the client’s expectations and needs. This method enables a great consumer-experience particularly for those customers that prefer digital interactions and the “do-it-yourself” approach by providing contextualized products and experiences, providing targeted financial advice, and diminishing the cost for consumers.
Credit soaring: AI for bank operation (an example)
In fact, credit scoring is not a new issue, and was actually one of the first application of statistical modelling in the banking sector. Nowadays, with the objective of measuring the credit worthiness of their clients, banks rely on gathering transnational data, statistical analysis, decision trees, and regression to better estimate a consumer’s credit risk and evaluate whether they will be able to repay a loan. The use of AI technology enables more accurate scoring and allows for enhanced access to credit by decreasing the risks and the number of false-positives and false-negatives. This will help banks to illustrate the most suitable debt plan for their customers. Furthermore, it ensures banks appropriately handle credit risk that is vital for financial stability. This is notably significant as there exist a number of supervisory requirements in this area, including the European Banking Authority Regulatory Technical Standards On Assessment Methodology for internal rating based (IRB) Approach . These technical standards aim to make sure consistency in models’ outputs and comparability of risk-weighted exposures.
Fraud prevention: AI for security purposes (an example)
As we know, identity theft, fraud, and security breaches are common to the banking sector because the sensitive personal data and money involved. Data security is essential to have a successful bank operations and maintain customer trust. Naturally, organizations use AI banking that is able to detect fraud quickly and more accurately without the risk of human errors overlooking any data or misunderstanding patterns. AI in banking detects fraud by referring to a pre-defined set of rules and by analyzing an individual’s past behavior. For instance, if someone who has previously made just only small purchases suddenly makes a very large one, the machine would flag that as fraud and contact the customer right away. Besides, AI is being used to authenticate and identify customers when they engage with their bank. So, banks are interested in investing in AI as a cyber-security tool to better prevent future cyber-attacks.
The Benefits of AI in Banking
There are some benefits can be indicated for utilizing AI in banking as follows:
1. Decrease in operational costs and workload
Initially, merging AI banking into operations, banks will diminish the needs for manual data entry and other human processes, which can probably lead to some errors. This not only saves time for the individual and the bank, but also eliminates costly mistakes. So, moving to conversational AI choices like virtual assistants will free workers from answering standard questions and managing basic transactions more appropriately. In contrast, bank workers can focus on higher-value tasks, such as deepening customer relationships and matching customers to the right services for their needs.
2. A new age of regulatory control
It is clear that banks are already one of the most highly regulated institutions in the world and must comply with strict government regulations in order to prevent defaulting or not catching financial crimes within their systems. With using AI’s, the ability to better detect fraud through behavioral analysis and integration with cyber-security systems would be improved, and banks can catch financial crimes much more faster with greater accuracy than humans, which puts them in increasingly greater compliance with regulations. In addition, it decreases the bank’s risk. On top of auditing customer behavior, AI in banking can log key patterns and other information for reporting to regulatory systems. Also, as Machine Learning in banking is used more frequently, expect to see financial regulations evolve with these changes.
3. Enhanced customer experience
Generally, AI in banking will be able to serve their customers faster with more productivity at all times of the day. Answers to questions and the ability to enact basic transactions will be at the customer’s fingertips. The trust between customers and their bank will likely grow over time via securer data and better regulatory compliance. By using AI, we can be able to provide personalized insights and connect customers to the right products and services for their needs at the time that they need them. Thus, the relationship between banks and customers will be evolved.
4. Boosting customer engagement
Another significant point is that Artificial intelligence can assist in the creation of customized and intelligent products and services, with new features, more intuitive interactions like speech and advisory skills like personal financial management.
In conclusion
Nowadays, even though Artificial Intelligence has had some outstanding aspects for baking sector, there are some challenges are still remained to tackle like ethical considerations. This article some aspects and reasons for using Artificial Intelligence as an advanced issue in the banking sector.
