Digital Banking Made Easy with AI
23 January 202420 October 2020 | Software
Today the banking industry and the broader financial sector face profound transformation, enhanced by next-gen technologies. AI, machine learning, advanced analytics, and more are already on the radar of many financial services providers. Adopting these enable a redesign of the operating model, helps reshape legacy systems or decode knowledge-driven opportunities hidden in already owned data stores. Ultimately, the next-gen technologies propel the financial service beyond its historical function to a standard of flexibility and personalization that digitally savvy customers demand. Such shifts become essential as new layers of disruption continue to be added up by non-financial, digital-only entrants (fintech) that compete in the banking market with new value and lower costs.
According to Deloitte, most of the banking and financial sector executives that have infused AI into their core business report a positive impact on their key strategic indicators: grow revenue, reduce cost quicker, increase operational efficiency, improve customer engagement and experience.
While there is a long way to go, many financial industry players are already taking significant steps to become fully-fledged digital organizations. Personalized lending or credit scoring systems are only a few of the desiderates that can be automated through technology. Read more in the article below, as we further analyze these use cases.
Table of Contents
- How can AI Transform the Financial Ecosystem?
- What Might AI Change in Back-Office Processes and Improve in Customer Experience
- What Obstacles do Banks Face in Deploying AI capabilities ?
How Can AI Transform the Financial Ecosystem?
AI helps banks to automate processes and plays a key role in the customer journey. Forbes predicts that by 2025 it will be vital for banking institutions to provide a seamless physical and digital interaction. To live up to the standards, introducing AI in the current processes, and upgrading the systems using the latest technologies is necessary. There are quite a few use-cases where AI can have a real impact:
- Credit scoring and underwriting – AI models can be used in banks to create a more complete customer profile by analyzing large amounts of data and making a more precise credit risk evaluation. Another advantage of these algorithmic methods is that they enable a faster loan application process and help with an increasing number of customers.
- Customer satisfaction – One advantage that AI has over human abilities is that algorithms can ingest and analyze enormous quantities of data from a variety of sources. With customers expressing their feelings about their experience using banking services on different digital platforms, having an AI always listening to their feedback can be a crucial help for banks to improve their offerings.
- Fraud detection – With an increasing number and diversity of banking offerings going digital, security is one of the prime concerns. Profiling approaches that can learn specifics about customer’s behavior, as well as anomaly detection solutions, already have very successful use cases to show for improving security and reducing manual reviews of potential payment frauds.
What Might AI Change in Back-Office Processes and Improve in Customer Experience
Banks and financial services companies can explore AI capabilities in multiple areas. As the use cases prove, AI can be deployed in most business processes and functions.
Case Study: Digital Banking Loan Application
CONTEXT – With the emergence of challenger banks and the customer’s behavior shifting towards digital solutions, the traditional pen and paper lending process is becoming obsolete. Banks and financial institutions that still rely on customers reaching their branches are losing market share to newcomers, who provide faster and easier experiences for their services.
SOLUTION – By analyzing your existing loan process, we can build a banking application designed to facilitate the lending process and reduce the physical time spent by customers at your local branches. By integrating the application with your core banking system and with APIs from several regulatory agencies or other third-party providers, the lending process will be compliant with all EU regulations and its workflow automated across the banking systems.
BENEFITS – The solution could help you remain competitive in the market, reduce operational costs in local branches, improve the customer evaluation process based on risk criteria and cut the time spent applying for a loan to a matter of minutes.
Custom Credit Scoring Using AI
CONTEXT – The loan value is directly related to how likely a bank thinks an individual or a business may default on that loan. Historically, credit scoring models were based on transaction and payment histories. However, at the moment, a more comprehensive user behavior can be analyzed because of the prevalence of digital mediums. Therefore, banks are turning to unstructured and semi-structured data sources, to capture a more nuanced view of creditworthiness and improve the accuracy of credit scores. However, the vastness of the data makes it almost impossible for human analysts to identify all customers with a high probability of default.
SOLUTION – Besides relying on transaction history, Natural Language Processing, Computer Vision and Machine Learning can help banks crawl through digital footprint data, such as social media posts, internet browsing data, geolocation, and other smartphone-captured information, to generate a more specific and well-grounded credit score for each customer. The solution we will build for you can integrate as many factors as feasible from a legal/data confidentiality perspective.
BENEFITS – This will result in speeding up lending decisions and processes while simultaneously limiting risks. The customer experience also can be improved significantly by offering instant online loan proposals and approvals. Another benefit is increasing market share for banks by using AI algorithms which are significantly more granular than conservative filtering.
Fraud Detection
CONTEXT – Detecting fraud is especially important in areas such as online shopping, online payment, and credit card usage. In order to combat fraud and detect patterns of anomalies, many financial institutions are turning to machine learning techniques such as logistic regression, decision tree, random forest, neural networks, and clustering. These algorithms can help detect fraud in banking and recognize inconsistencies or inaccuracies in payment and application information.
SOLUTION – We will train the model on a continuous stream of incoming data to have a baseline sense of normalcy for the contents of banking transactions, loan applications, or information for opening a new account. The software can then notify a human monitor of any deviations from the normal pattern so that they may review it. The monitor can accept or reject this alert, which signals to the machine learning model that its determination of fraud is correct or not. This would further train the machine learning model to “understand” that the deviation it found was either fraud or a new acceptable deviation. The flagged events might include an unusually large funds transfer or a payment made in a location that the customer isn’t known to frequent, as it is common that fraudulent transactions occur far away from where the account owner lives.
BENEFITS – Banks could benefit from a machine learning-based fraud detection solution in that they would be able to instrument it across more than one channel of data to be analyzed. This would mean the model could be trained to detect fraud within more than one type of transaction or application, or both at the same time. AI can increase detection of real fraud by 50%, and let clients refocus their time and resources toward actual cases of fraud and identifying new fraud methods.
Customer Feedback – Sentiment Analysis
CONTEXT – In our days clients express their point of view on a diverse range of digital platforms, which can be used for constructing a final multi-modal customer feedback.
SOLUTION – Natural Language Processing (NLP) based software that analyses incoming customer feedback from surveys, feedback forms, and social media to identify customer sentiments. For example, if a bank has several million responses from customers to their open-ended text-based feedback form, NLP can help in cutting down the time taken to review these messages and unearth new insights about what customers really want. Banks would upload existing customer messages to an NLP software to first categorize this data. In the case of sentiment analysis, the software’s algorithms will be designed to parse out sentences, phrases, and other significant parts of each customer’s message and automatically tag these categories as a positive, negative, or neutral sentiment.
BENEFITS – With this information, the software can then be designed to output insights for several different objectives. For instance, the software can be designed to identify top customer issues and complaints or what products customers are talking about and how. For example, using NLP software to read through customer feedback on social media, banks might identify that customer posts from one particular geographical region are unusually high. NLP software might help identify that top customer issue is password and login issues. The bank can then alert the IT team in that region to take action to resolve the issue.
What Obstacles do Banks Face in Deploying AI capabilities?
Deloitte surveyed over 1.000 executives from US-based companies that are prototyping or implementing AI. The responders highlighted a shortage of specialized skill sets required for building and rolling out AI implementations:
- Software Developers: 16 – 34%
- UX Designer: 22 – 41%
- Transformation Experts: 27 – 22%
- Data Scientist: 27 – 30%
This is where software companies, like Fortech and Zoom AI, join the digital marathon and start helping organizations prepare for AI adoption. With an agile state of mind, the two companies can help organizations build digital lending solutions tailored to their needs to improve operations and meet the evolving demands of the digital environment.
Fortech and Zoom AI have more than 20 years of combined experience, with a proven track record of successfully encompassing stability and agility in all their client relationships. Leveraging the in-depth AI knowledge with the broad software development know-how in most technologies, together we create added value by offering end to end software services for our clients.
Leveraging a quality-focused culture, over a decade of experience in financial software development, Agile, and modern technologies, we support and accelerate digital innovation.
