Commercial Banks Five Ways to Grow Your Business and Cut Costs
In today’s complex and rapidly changing environment, companies face the same challenge: to grow their business while reducing their expenses. In this blog, we will review five ways commercial banks are approaching these cost savings for the municipal segment.
- Finding new lending and investment opportunities through municipal market prospecting
Finding new customers is the starting point for growing your business; however, it is essential to ensure that the quality of the credit matches your expectations and that the associated risks are aligned with the pricing of the loans. The first step to growing your loan/investment portfolio and identifying new creditworthy clients/investments is to facilitate the due diligence process. We suggest:
- Access credit scores – For entities with credit ratings, you can quickly understand the overall credit quality by knowing the rating. Unfortunately, there is no publicly available aggregate information on the credit quality of unrated entities. And if you’re a commercial bank, these might be your main customers. For this, many find preliminary credit scores helpful in learning more about the credit quality of rural communities, small towns, school districts, water and sewer authorities, and other municipalities.
- Using a search or filter tool – PFAST aims to create reports based on different criteria such as geographic location, segment, key financial ratios or credit ratings.
- Peer review – Benchmarking your prospects against a peer group can be helpful in seeing how well they are performing financially and economically in the segment and region they operate in. You can use financial and economic benchmarks to compare entity performance against national benchmarks for each risk factor.
- Automating the data collection process and improving speed
It’s no surprise that collecting data for any credit risk assessment is time-consuming and difficult. For economic data, you can consult many government sources to find income, unemployment, and population data. When it comes to financial data, no matter where you get the financial report, you need to collect key financial data points from different statements and a single year of data may not be enough to see trends at over time.
Today, with Artificial Intelligence, extraction models and a dissemination system, it is possible to automate the extraction of financial and economic data. At S&P Global Market Intelligence, we worked with our data scientists to create mining code that automates data collection of financial and economic data points. We go through various data extraction processes each month and add all new information to our database to ensure that our customers have the most up-to-date data. With this, analysts can focus their time on what they do best, analyze data, perform forward-looking scenario analysis, and focus on what matters most.
- Dissemination of features to free up resources
Once the data is extracted and collected, you also need to ensure that the data flows through your internal credit and monitoring systems. For this you need:
- A unique identifier: Economic and financial data must be associated with a unique issuer/issuer/debtor identifier. For investment portfolios the most common is CUSIP, but when it comes to loan portfolios you can have your own ID or some use our ID, the S&P Capital IQ ID.
- Municipal data in a database: Historical economic and financial data points extracted for each year should be stored in a database. This database must be secure to comply with data sharing and privacy regulations.
- Integration with your credit models: Once you have the unique identifier and the database, you need a mechanism to retrieve the data and integrate it into your credit models. We have developed flexible delivery mechanisms for municipal data: S&P Capital IQ and Capital IQ Pro plugins, API and Xpressfeed.
- Rationalization of portfolio management
Credit quality monitoring is a time-consuming task and in accordance with the Dodd-Frank Act, you should make your own assessments to have your own opinion on your assessed and unassessed municipal exhibits. Even though they are all investment grade, you may want to know if the credit quality deteriorates as soon as possible, as this can lead to price and credit risk.
To solve this problem quickly, it is essential to have quick access to the latest financial and economic data to generate timely credit scores. At S&P Global Market Intelligence, we have developed a portfolio tool for municipalities. Through this process, you can assess your municipal portfolio in minutes by simply entering a CUSIP or S&P Capital IQ ID, and you can generate risk scores for local government, water and sewer, and healthcare entities. health.
- Optimization of loan pricing
When your bank makes municipal loans, provides financing for bonds, notes, or any other need, you want to make sure you’re pricing it correctly. If you use a credit scoring system and a risk-based loan pricing model, you should perform a thorough analysis of the credit risk associated with the municipal entity. You want to ensure that loan pricing aligns with the municipal entity’s credit risk.
To this end, S&P Global Market Intelligence has developed the Public Finance Automated Scoring Tool or PFAST. This credit scoring solution provides a granular, transparent and consistent framework for measuring and evaluating municipal credit risk.
We provide an efficient approach to navigating the current climate for low-default portfolios which, by definition, lack the detailed internal default data needed to build statistical models that can be robustly calibrated and validated. We leverage S&P Global Ratings’ Public Finance Rating Criteria analytical process to determine an overall public finance risk score, which is mapped to historical default and collection data dating back to 1986.
Using a solution like PFAST, banks that use risk-based pricing can competitively price the best loans for all borrower groups and reject or premium-rate loans that represent the highest risk.