How Data & AI are Transforming the Future of Banking

Overview

The banking industry is at the forefront of adopting big data & AI solutions. According to IDC by 2022 over USD 5B is expected to be spent by the banks on AI applications, including automated threat intelligence & prevention systems and fraud analysis & investigation systems. Such technologies are gaining traction mainly due to AI’s ability to improve administrative and customer service levels, provide predictive analytics functionality, improve fraud detection, automate, and accelerate core functions while saving money all at the same time. Further it is expected that adopting AI powered solutions will help banks save over USD 1T by 2030 worldwide.

Bid data & AI are typically applied across the Front Office — enabling conversational banking with the help of chatbots; Middle Office — implementing a bulletproof fraud detection and prevention system; Back Office —introducing software to efficiently analyse and manage transactions, customers, and other data.

Banks need to strategize their focus area and identify where data & AI can have the greatest impact. They should structure and streamline their data by setting up data warehouses or data lakes enabling easy access and integrate analytics with decision management system for optimum services. Banks should have the right talent tool of data scientists and specialists to maximize benefits of data & AI.

Key Benefits of Big Data & AI in Banking

Cuts operational costs: Big data, AI, ML, and NLP technologies bring automation into bank workflows helping perform repetitive tasks more accurately than humans. This intelligent process will ensure minimum operational costs and avoid errors in the process. Further, AI chatbots provide 24*7 customer support that helps increase credibility and improve customer experience.

Improves customer support: Data & AI helps understand customers better by decoding the next decisions and create a personalized container of information for each customer. This helps banks customize buyer experience and improve customer satisfaction and loyalty.

Detects fraud and money laundering: Data & AI platform help enhance banking fraud detection by helping machines recognize frauds like email phishing, spoofing, etc. AI algorithms help machines understand the pattern of historical frauds and helps combat similar ones in the future.

Improved loans and disbursements: Data & AI powered banking apps help reduce the risk level in disbursing loans. Such solutions also help in faster and quicker loan application and loan approvals improving creditworthiness.

Regulatory compliances: As the banking industry is susceptible to constant changes in regulatory and compliance rules, it becomes imperative for banks to stay updated with ongoing compliances. Data & AI based systems help banks remain process compliant and offer services as per the regulation guidelines.

Application Areas of Big Data & AI in Banking

The tech-driven business models used by banks use machine based operating environment. Data & AI are being widely accepted and deployed by the banking world and application of these technologies is making operations easier, faster, and secure for banks. These applications include:

Automated customer onboarding and processes

Data & AI models help in faster, quicker, and automated customer onboarding, reducing operational costs and fuelling faster execution of processes that follow.

NPA Optimization & credit risk management

Analytics and insights drawn AI based analytical engines help in business optimization as they are aligned and tested with existing portfolio of NPAs, helping assets to be either subcategorized or be written off. Such solutions help in the evaluation of credit worthiness of customers owing to their precision in credit evaluation and lending decisions.

Risk management and security

Checking financial status, document verification, and releasing loans are risk-related activities for bankers. Data & AI-based mobile banking applications can make these processes more accurate and secure enabling reduced possibility of fraudulent activities.

Chatbots and customer Support

Having a pleasant customer experience while interacting with a bank is considered a success for customer support. For this chatbots are the most preferred solution as it potentially automates and replaces all straightforward front-office operations, from KYC to customer helplines.

Data security

Credit card fraud is the most common type of personal data theft. Most of the banks are adopting security systems which track customer behaviour, location, and habits, triggering an automatic secure mechanism for unusual activities. This prevents theft and data breaches and promises safer and efficient operations.

Market analysis and tendency

Data & AI models enable banks to process data efficiently and predict market trends, stocks, and currencies. Advanced models further help evaluate market sentiments and suggest investment options enabling speed to decision making. Such solution is being widely deployed by hedge fund managers as it flags the right time to invest in stocks and warns when there is a risk.

Key Trends Driving the Increased Demand for Big Data & AI in Banking

Rise in customer expectations

As customers these days expect more personalized services suiting to their personal needs, banks are becoming more customer centric and focusing their digital transformation initiatives around them. Every customer expects to have a seamless experience at the bank without wasting much time. For this, banks are investing heavily in analytics and AI solutions to better suit to the customer needs.

Acceleration of cloud banking

Cloud computing solutions are gaining traction in the banking industry due to its benefits for storage, scalability, agility, and flexibility. Gartner predicts that cloud-native platforms will serve as the foundation for more than 95% of new digital initiatives — up from less than 40% in 2021.

Need for compliance and data security

The need to protect its confidential data, prevent fraud and enhance behavioural profiling is leading banks to invest in capabilities which act as a firewall against any threat. Regulatory compliance is of another importance for banks and non compliance to it can have severe consequences. Banks are thus investing heavily in AI driven solutions to provide extra protection to the banks against any breaches.

Increase in investment

Banking industry is ranked second, slightly outpaced by the retail industry, in investments made in data & AI solutions. Banks and credit unions are the largest spenders on big data analytics and AI/ML solutions owing to the benefits harnessed by such solutions.

Preparations Required by Banks for Switching to Big Data and AI

Adoption of big data and AI like cloud is picking up pace in the banking industry. However, there are few steps which a bank needs to be vary of before implementing these technologies which include:

Develop an AI strategy

Banks need to evolve from implementing AI on a piecemeal basis to embedding AI as part of the core strategy in the organization’s culture. Banks should establish an AI vision and move from simply being AI-aware to becoming a strong AI adopter.

Define a use-case driven process

Banks need to identify business-value-driven use cases for data & AI and prioritize them into a roadmap to achieve the desired goals defined during the AI strategy phase.

Experiment with prototypes

Banks should adopt a forward-thinking approach and consider prototypes as a first step down the path of future AI success. Banks need to consider prototypes in the context of an entire solution or ecosystem (e.g., customer experience and growth or call centre optimization) and not as an isolated functionality to fit with other building blocks within the organization.

Build with confidence

Banks embracing data & AI need to start putting trust at the centre. They need to identify and define a new balance in controlling and converging their data while exploring new data sources and partners.

Scale for enterprise deployment

Banks need to build a centralized talent pool comprising of data scientist, user experience designer, data engineering manager, analytics visualization developer, and analytics manager for an effective implementation. Banks should employ AI models, integrating them with current processes, and mould them as business processes change for firmwide scalability of models.

Drive sustainable outcomes

Once implemented, AI technologies should help how models react to various inputs and identify ways to improve results. These lessons can then be applied to development of AI systems across the bank for fruitful results.

Use case/Implementation of Big Data & AI in Banking

Bank of America has deployed analytics & AI in fraud management to run better algorithms and to understand what true fraud is. It has also deployed the solution in trading and offering more refined services to its clients. It is also using AI in its hiring process to identify the best talent and in chatbots to provide utmost personalized services to its customers.

Aumni Inc., an analytics platform for private-capital markets, raised USD 50 M in a funding round led by JPMorgan, signalling its appetite for financial-analysis tools powered by AI. Aumni’s AI software combined with human expertise can extract and analyse deal data that is buried in investment documents and legal agreements, generating insights on metrics including investment rights, fund performance and emerging investment patterns.

Citibank is building a practice called audit of the future where cutting-edge ML, NLP and advanced analytics solutions are available through IBM Watson Discovery, IBM Cloud Pak for Data, and IBM OpenPages with Watson to transform the daily work of the bank’s 2500 auditors.

Way Forward with Xebia

Xebia helps create a roadmap on how to build, design, and modernize data platform that suits to the needs of an organization and helps create short/mid- and long-term investment plan. Within the data & AI vertical, it provides data science and data engineering, analytics translation, AI and Machine Learning, and data business consultancy and training and managed services.

Xebia uses its own templates for each public cloud and deploys a data platform which helps cut the time required to gain valuable insights into business/customers. Its analytics engineers build reliable data pipelines which enable self-service reporting and visualization.

It has a strong partner network consisting of Google, Databricks, Microsoft, AWS, Snowflake, which helps reduce customer churn, predict demand, optimize logistic chain, and automate business processes.