In recent years, we can witness that artificial intelligence is becoming a need in every domain of the industry, and AI’s different domains, such as computer vision, natural language processing, and predictive modelling, are helping humans solve their use cases and problems more effectively and without the intervention of the humans. We can also enjoy the intervention of AI in our daily life, and humans are becoming more curious about this intervention. Banking sectors are also positively affected by the intervention of AI. In this article, we will cover some of the critical use cases of AI in the banking sector that is helping humans advance the banking sector.
Customer Engagement
This sector implies AI-enabled models to assist the customer during onboarding. These models are trained to perform step-by-step processes of customer onboarding. Natural conversation utilises digital channels. Bots are there to automate the services like processing the documents of customers and taking images from computer vision programs.
Some other vast implementations of OCR systems can be found to process the documents of the customer, and a variety of systems are there that help in performing document verifications of customers to make the customer onboarding faster and smooth. Since no human intervention is required, fewer human skills are necessary. It also helps the service provider to prevent their human workforce from performing repetitive and sometimes mundane tasks inside the premises.
WeChat messenger is an excellent example of engaging customers by utilising conversations. For instance, China Merchant Bank is one of the largest credit card companies using WeChat messenger to handle 1.5 to 2 million customers.
Customer Service
In a banking system, customer care, service and engagement programs play a crucial role in democratising the bank’s services among the customers and also help in enhancing sales and marketing of banking services. Nowadays, it is found that chatbots have replaced humans in customer care, and this also impacted the banking sector. While applying chatbots in such systems delivers a very high ROI in cost savings.
Embedding chatbots in the banking sector can be considered the most common application of AI in the banking sector. There are various tasks such as balance inquiry, statement production, and fund transfer that can be managed using these chatbots and applying such chatbots helps in reducing the overload of the channels like contact centres and internet banking channels.
HDFC bank’s chatbot named EVA is an excellent example of this use case where we can consider it as the banking assistance for the customers of HDFC and helping them with things like Branch addresses, IFSC codes, loan and interest rate information.
Automatic Advice Systems
In traditional banking and finance systems, we find that banks employ humans to advise the customers and clients regarding investments, loaning, credit cards and debit card schemes. However, as the number of customers and their requirements increases, it becomes challenging for humans to manage such big data and provide beneficial advice to everyone.
Artificial intelligence and data science intervention in banking and finance systems have made this task very easy. AI-enabled models are being developed and prepared to perform automatic advice procedures with a touch of personalisation for everyone. These models are learned to give the best advice to customers according to the requirements, and that makes them a personalised advice system in banking. Also, they are beneficial in reducing the time of the banking procedures.
For example, ICICI banks in India blended an RPA( robotic process automation) with their systems and successfully cut loan processing time in half.
Predictive Analysis and Modelling
One of the most common use cases of artificial intelligence is to perform predictive analysis and modelling using the data. These modelling procedures find the correlation between the data points and variables and tell about the future possibilities. Traditionally it was complicated to understand and predict such possibilities, and the intervention of AI models made it very easy. Using such models, we can have information about sales opportunities, cross-sell opportunities, share market statistics and social media channels. These pieces of information can lead to a direct revenue impact. We can also calculate metrics around operational data in the banking system so that predicting values can become an easy task. There are various examples of static models and neural networks being consumed frequently to predict near or far future trends and patterns.
Security
AI can be utilised for security purposes, which not only helps in the banking system but also allows being applied in different domains. For example, we know that models work by understanding patterns hidden in the data, so if data about the previous threats are given to these models, they can help us find current and future threats. Furthermore, these predictions can help baking systems to prevent attacks and fraudulent activities.
There is various software such as SEON, SAS, ThreatMetrix and Feedzai that is helping in preventing banks from threats and fraud. For example, Citi bank employs Feedzai for three significant protections: securing account openings, controlling transaction fraud, and stopping money laundering.
Security on the premises of banks is also the main topic to be covered when it comes to security. These security purposes include enabling security cameras and security guards throughout the premises. Artificial intelligence also helps these systems to enhance the level of security by utilising face recognition and biometric systems. However, these systems can also be integrated with bank applications to enhance the security on the customer side.
Credit Scoring
One of the major impacts of AI on the banking system can be found in credit scoring, where the model trained using the history and demographics of users can help determine the creditworthiness of clients and customers. Such an application becomes very helpful for the banking systems where they can predict the credit scores in a very robust and accurate way.
Since demographics are applied in training, these models sometimes don’t require a vast history of the users and can work by comparing the demographics of the other users to tell the credit score of a new user.
Softwares such as GiniMachine, LenddoEFL, ZestFinance, Kreditech and SAS Credit Scoring are leading AI-enabled software for credit scoring.
Decision Making
Various decisions can be taken using historical data, which can have a high impact on the banking systems. Such models work in a system where expert data is stored in a database and utilised to make strategic decisions like deciding workflow between different departments, cash flow management, and document flow strategy within the bank.
Various banks are utilising analytical AI-based tools such as AlphaSense, which is an AI-enabled search engine that uses the processes of Natural Language Processing and also helps with task routing.
Conclusion
In the banking sector, the intervention of artificial intelligence has put a lot of scope in various tasks in the knowledge workforce and in preventing banks from cyber risks and fraudulent activities. Enabling AI in banking sectors has enhanced the competition between different banks. AI models are improving banking services from being applied to customer services to making impactful strategic decisions.
About DSW
Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.
DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.
Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai