Banking

Banking

We empower banks to provide personalized, frictionless customer experiences, automate processes, drive profitability, enhanced credit scoring, and do lot more using the latest AI and ML technologies

From introducing ATMs in the 1960s to adopting online banking in the 2000s followed by ‘mobile banking in the 2010s, banks have always been at the forefront to adopt the latest technological innovations and redefine how customers interact with them. And with reports estimating that AI technologies could potentially deliver up to $1 trillion of additional value for global banking each year, banks that fail to make AI central to their operations and core strategy will risk being overtaken by the competition.

AI technologies can help banks boost revenues through increased personalization of services, lower costs through automation, reduce errors rates, increase efficiencies by better resource utilization, and most importantly use predictive analysis to uncover new and unrealized opportunities for the banking sector. Features such as AI virtual assistants, digital payment advisers and biometric fraud detection mechanisms lead to higher quality of services at scale.

Retail Banking

Retail banks are among the most sophisticated users of AI and ML among all other industries. From automated loan approval process to personalised customer experiences, targeted marketing capabilities, and a variety of other solutions, retail banks are adapting AI to optimize every part of their business and to manage risks.

And they are doing it out of necessity. With the exponential increase in the number of fintech companies gobbling up market share, retail banks must adapt to AI to keep up with them.

Here are a few ways TheDataTeam’s AI and ML services can help banks beat the competition and increase revenue –

Automation of Processes

With TheDataTeam’s RoboticDataSciene, banks can automate a significant percent of their repetitive work processes, allowing knowledge workers to dedicate their time in important, value-added operations.

Corporate Banking

Corporate banks must radically expand their use of AI and ML to optimize every part of their business, from portfolio management, customer acquisition, relationship deepening to managing delinquent loans. Some of the key uses of AI in corporate banking include:

Telecommunications
Continuous Risk Monitoring (EWS)

With a significant increase in the percentage of NPAs globally, it is imperative for banks to reconsider their credit risk monitoring practices and replace the manual process with an AI-powered Early Warning System(EWS ) that helps in identifying risks at an infant stage. A Cadenz-powered EWS can help banks in not just effective credit risk monitoring but will also serve the related purposes of fraud control and regulatory compliance.

Retail
Identify New SMB/SME Lending Opportunities

Corporate banks can use AI to manage and automate credit scoring. With the help of predictive analytics, the AI software can calculate how much risk the bank would have if they chose to give a particular company a loan. A company’s past credit history and future growth potential may be used as indicators to determine if they will make their payments on time and pay their loans back in full. With Cadenz, the company can use a large number of datasets for credit scoring, which can help corporate banks create new lending opportunities for SMBs and SMEs.

Oil and Gas
Fraud and Anomaly Detection

Corporate banks can use AI applications to detect possible fraud cases within payments, account receivables, loan application and other financial and contractual agreements. The machine learning model scans each data point and learns how they relate to one another in real time whereas the predictive analysis-based applications are allowed to scan through the bank’s database and they make correlations between data points to come to their own conclusions. AI-based anomaly detection applications can recognize fraud within loan applications and other financial documents by searching for any discrepancies in financial information that the applicant company provides or any financial information the bank already has on the customer.