By: Kedar Bade, Senior Consultant, LTI

The Finance Industry, especially banking across the world, is historically vulnerable to the losses due to frauds. Frauds amounting to billions of dollars are managed with the connivance of handful of bank staff. They use the lacuna within the internal operations and systems which carry inadequate internal controls. Considering the huge funds involved, high risk portfolio, coupled with cut-throat competition, along with outdated technologies and methods, banks carries huge credit and operational risk at the same time. This actually underlines the importance of large scale system upgrade for banking in general and corporate banking in particular. In such scenarios, digitized and well-synchronised banking system should be in place.

Need for digitization
One of the main causes for lack of standardization is variety of credit facilities required with multiple internal combinations as per the business need of the customer. Unlike retail banking, corporate finance is generally through multiple banking, consortium banking, or through syndication.

Risks and technology solutions for the various stages of business flow:

Initiating relationship
This includes the relationship manager building a banking relationship with a corporate. Corporate routes its business through the bank, and generates the track record.

There is a possibility that the information gathered by the Relationship Manager (RM) through his sources, contacts, may not be always reliable, and it needs to be backed by data. This is possible by using the latest technology of gathering information through various sites. For example, the background of the management or any attrition of any key resources can be tracked through professional social networking like LinkedIn, Glassdoor, etc. The background for the company can be gauged through followers, likes, or comments from social networking sites like Facebook, Twitter, etc. The feedback about the product from various other sites will need to be segregated and analysed using various Big Data tools and techniques. However, it will give the information not provided by the client. It can also predict the probable risk the client or industry may be facing, which may result in financial loss.

Building relationship
The process includes relationship manager preparing the loan proposal, risk team evaluating it, managing the team, approving the proposal, ensuring the documentation completeness, and finally the operations team disbursing the funds.

There are many events happening when multiple banks end up in providing excess finance to the borrowers, due to lack of co-ordination amongst themselves. This can be avoided by using the blockchain technology when all the banks in multiple, consortium banking are informed of any finance given to the borrower. This can also be providing vital information of the realization record of the exporter’s dues across banks.

Maintaining relationship
Monitoring the operations on the account, while ensuring it is within the norms set by the statutory authority.

This process includes monitoring the transactions on the account, receipt of stock statements and drawing the inferences on the operational efficiency of the borrower. In majority of the banks, it is a manual process where the operations on the account are monitored manually, leaving gap for human errors.

Machine Learning (ML) and Robotics Processing Automation (RPA) could be the most effective technology solutions for reducing the operational risks. Through ML, the system can identify the pattern of the borrower’s operational efficiency using stock levels, realization records, client selection, and provide alerts. It can highlight the risks based on certain parameters like advance as to % of the export bill, availability of limits, whether the stock statements are received, if the importer / exporter or its country is not in the watch list, etc.

Also, with the help of the select key parameters like Industry, product, quantity, rate, consignee from the invoices, documents provided, etc. the system can see the pattern for trade finance for specific borrower or the industry. Using the Natural Language Processing (NLP), various operational processes can be automated in trade finance and corporate loan arena. This will reduce the cost of documents handling, while reducing the risks, and can bring in considerable reduction in time per transaction.

To conclude, any or many of the above technology solutions can help in mitigate the operational and credit risk present in corporate banking, or trade finance area of banking operations.