Banks’ AI and data science capabilities should help corporations achieve the goals of meeting liquidity goals and working capital needs and increasing business margins. The ever-growing business needs of the corporations will require new banking products and services, and data science is the key to fulfill next-generation corporate customer needs. Satisfied customers would bring in more customers and would be more inclined to do business with the bank.
For years, banks have provided solutions to the financial problems of corporations through standard products, a relationship that has been mostly transactional. With banks sitting in the middle of a huge pool of data, artificial intelligence (AI) and data science will change the commercial banking experience forever. Now banks have the capability to use analytics and AI to provide their clients with key performance indicator (KPI)–driven real-time business insights and play the role of advisor and partner to their customers. This new era of banking will be driven by digitization and business-inclusive banking .
AI and Data Science: Future-Proof Banking
If deriving more customer value from existing corporate clients is the bank’s key strategy, AI and data science have a pivotal role to play in such endeavors. AI and data science–driven analytics systems can offer real-time advice to improve corporate business efficiency and help mitigate risk by analyzing changing corporate needs and goals, transaction data, and external market trends.
In the late 2000s, banks offered only the top companies relationship-driven services. With ever-increasing data being generated by social networking sites, balance sheet information, credit ratings, market news, and market sentiments, data analytics is poised to play an important role in understanding the customer’s business model and monetizing such information. A majority of SME and business banking segment clients can’t afford to use a treasury desk, so banks can offer treasury services to these corporate segments using AI and data science. Banks need to use the AI and data science platform to benefit their customers by offering
- Business-specific product offerings and customized pricing models
- A frictionless journey across the transaction lifecycle and KPI-driven business insights
- Lower transaction costs and faster transaction execution capabilities
- Business portfolio and risk management analytics
The Impact of AI and Data Science on the Traditional Banking Business Model
The advent of AI and data science would eventually mean a natural death for standard monolithic commercial banking products and the evolution of an advanced product line. Better liquidity management using data science, business-centric services, dynamic pricing of services derived from microsegmentation, AI and data science–powered market insight dashboards for corporate clients, AI-driven portfolio and risk management, and business modelling tools are some of the most revolutionary offerings the banks can supply using AI and data science. The overall effect would eventually be the transformation of banks from mere sources of funds to being strategic allies of their corporate customers.
Digital Product Store for Corporations (Shopping Cart–like Experience)
The idea of a digital product store for companies is akin to the product cart available at popular e-commerce sites. Here, a customer can select an account type (say, a checking account) and then choose a host of products that suit their needs. They may choose from a variety of products like netting, cash concentration, pooling, drain pooling, virtual accounts, and so on. AI-based relationship managers can come in at this point to suggest appropriate products to the clients, thus facilitating cross-selling and bundling of products.
The selection of products would be followed by a product viability check based on the client data and advance analytical models to ascertain the credit risk associated with the client. Finally, the client can add the desired products to their cart and a dynamic pricing engine can apply the custom rate or charge to the client. The basic regulatory checks like customer due diligence (CDD), product due diligence (PDD) , and know your customer (KYC) can be performed online. This is the only way banks can offer real-time banking product setup and transaction capabilities to customers.
Banking Product Lab for Product Managers (AI and Data Science–driven Product Modeling)
AI and data science can help product managers immensely. The application of advanced analytical models to data from various sources, including, but not limited to, transaction data, data in the news, social media, company databases like Bloomberg and Thomson Reuters , and so on, can help product managers design the right product portfolio for the client. This knowledge also presents an opportunity to bundle various products as well as cross-selling opportunities. AI-based systems that interact with the customer (AI chatbots, etc.) can automatically recommend products tailored to the specific needs of the client.
KPI for Dynamic Pricing and Customer Satisfaction Index Review Using Big Data
The analysis of the myriad of transaction data available about the customer can be used to generate customer business KPI and perform back-end feasibility checks, thereby reducing the probability of default and eventual non-performing assets. Relationship managers can use customer transaction data for dynamic pricing of products based on the credit analysis of the customer, thereby compensating the bank for the amount of risk taken for each customer.
Apart from these applications, a KPI-driven customer happiness index could be tracked to make sure the customers are satisfied with the services of the bank, increasing repeated business.
AI-driven Commercial Banking (AI Bots, the New Relationship Managers)
The ubiquitous rise of AI in the banking and financial services space has brought unprecedented changes in commercial banking. By 2020, customer relationship managers will be replaced by AI bots that take care of client onboarding, product recommendations, cost-benefit analysis, service activation, transaction initiations (with repeated transaction pattern analysis), etc.
The Final Frontier and the Way Ahead
“AI and data analytics will not only transform the way banks engage with clients but also the relationship that the bank has with its corporate clients. AI and analytics have the potential to make banks strategic partners to their corporate clients. AI and data science will help corporate clients drive their business and operational efficiency through KPI across the product line and thus extend the power of AI and data science in bringing about the change and making the art of doing business more efficiently.”