Dynamic pricing or charges for corporate banking services

Inspiration

Inspired from credit card limit enhancement process.

What it does

To avail any of corporate banking services, corporate customers need to pay charges, and the fees are calculated at FRM or product level depending on transaction type and other parameters. This static process doesn’t have flexibility to provide best benefits to most trusted customers. The charges should be determined by the risk associated with customer rather services used. Integrated solution for corporate banking provides access to cash, trade, and corporate lending thru a single sign on for bank users and thru fusion corporate channel for corporate customer. Financial Institution will have access to all the transactions and can get the behavior and financial muscle of the corporates. AI enabled system should consider the factors which would provide insights of balance sheet and payment behavior, here are few examples of those factors.

How the other companies are performing within similar range of net worth.
How is the global market for that category’s product?
Behaviors for the previous loan repayment.
Recent time if any interest payment defaulter
If the company is having major transaction with another bank And based on this grouped them (for example A+, A, B+, B etc..).
While invoking any financial instrument, the charges should be determined based on customer category rather services availing by customer. If the risk is higher, the higher percentage of charges would be applied and lower percentage for top rated or safe customer. AI enabled pricing algorithm will provide best interest rate based on the risk appetite. For example, when a LC is initiated, Charges that will levied by the ISSUING BANK and normally payable by the APPLICANT include
Opening Commission,
Negotiation/Payment Commission,
S.W.I.F.T message fees,
Amendment fee,
discrepancy fees,
delivery order fee,
courier fee
outstanding query fee Among the charges, there are static charges which is determined by central bank / the governing body, and others are dynamic which varies bank to bank. By making those variable charges dynamic based on customer transaction behavior would lower the risk for any financial institution and provide best benefit to trusted customers. Similarly, the same customer grouping could be utilized while proving loans to corporates. This could be included for lending products as well to lower the risk, and bank can get a insights of their assets (loans) to manage NPA and balance asset liability ration.

How we built it

Challenges we ran into

What are all the factors need to be consider to get insights of customer's balance sheet.
How to get external factors or parameters that paly role in customer's balance sheet.