City as a Financial Institute

A financial institution is a company that focuses on dealing with financial transactions, such as investments, loans, and deposits

When we talk about financial institutions few things that pop up in our minds are banks, insurance companies, regulatory bodies etc. But going by the definition, any company or government body that has any financial transactions is a financial institution.

The government of any city when observed as an entire entity on a daily basis undergoes thousands of transactions whether it be related to services, networks, ongoing projects and many more activities that occur every day. In fact, when a city is taken as whole, the transactions are much more than the average Bank or multinational corporation.

Despite of a city technically qualifying as a Large-scale Financial institution, it is often ignored. A major reason for concern is that the efforts and processes that are taken into account for mainstream institutions are ignored for governments. Which generally expose the city to a huge risk in the form of fraud, ill-advised investments.

Granted that every minute transaction that a city undergoes on a daily basis can’t be safeguarded. But when talking about major investments that a city government does in the form of projects such as metro constructions which involve a lot of private firm participation and transactions of huge amounts of money should be analysed properly for risk.

In reality, any new project that is undertaken by a city is an investment made by it in public welfare. And the investors are its citizens. There currently are processes which analyse the risk involved in such transactions. However, they are not as comprehensive as those taken by other institutions of much smaller sizes.

For example, when considering any large-scale transaction undertaken by a city, let’s assume the construction of a metro line. The winner of the bid was a Japanese company. The city exposes itself to: Credit risk, Market risk (volatility of currency), Operational Risk and Liquidity Risk. In most cases it is not fully safeguarded.

Credit risk is mostly considered for banks and loans. But it’s also applicable here. When a Bank is considering a loan application it generally uses a combination of factors to decide on the specifics. This can easily be transposed for transactions made by the city as well.

I’ll be considering the Linear Regression model of credit risk calculation. The model is:

Linear regression Formula

Here, PD is the Probability of Default (non repayment of loan). X and B are variable factors to be considered like Income, Age which will be essential for loan repayment capability or in case of the city, return on investment.

So, if we want to apply this model for a city, in particular for a transaction such as the construction of a new service. We would want to start the process of risk elimination right from the beginning. That is, while selecting the private firm that will undergo each phase of completion of the construction. Obviously, the city will receive a lot of bids at each step. To select the best option among those, that is the least risky, we could run the data through a code similar to one used by banks.

So, I have used R studio, but it can similarly be run through Python. I preloaded the package rms.

Regression code

So, I have used a pre-existing library ISLR, which is a book and has a lot of statistical tools. LRM is linear regression model which is a tool part of the library which will be used.

Default is just a data set containing a number of user data related to 3 variables: Student status, Account balance And Income. (for loans)

So, in transposition to a city, it might become three different variables like: 1. Foreign company (will determine the need for calculation of market risk. a yes/no question). 2. Annual turnover 3. Bid amount (Both will account for investment security). This is just an example of how the process would work.

Running it through the code, we end up selecting the bid which is least prone to Credit risk (loss in return of investment).

Another issue that this process addresses very efficiently is the conversion of data to a balanced Data set with defined sub sets (variables like bid amount)

If the given data is unbalanced, the data provided by the code is not always accurate. So,

Balancing tool

Again, used a pre-existing package, DmwR. Under the package to balance the data set “Default” I’ll be using the SMOTE method. Basically, it will balance the class. As mentioned, “Default” is just an example of a data set its specifics are:

Unbalanced random Data set.

So, to Balance the Dataset and make it useful to us, I will SMOTE it,

Balanced Data set

I Defined the new balanced data set as City data. The tool just balanced the data to us. Now we could apply the LRM tool to this data set and we will get accurate results which will be the Firm with the least Credit Risk.

Applying lrm to City data

Hence, we can make safer decisions using this process exposing the city to less risk. Other than LRM, there are a lot of different statistical tools that can be used to the same effect.

Traditionally the above process is used to safeguard banks and other institutions. But the fact that technically a city can be considered as a huge financial institution, it should also be applicable here.

In the end any transaction that a city makes as an institution is just investment of taxpayer’s money in a service or utility. So, it’s important that the decisions made are safe and properly analysed.

If you find any errors or have any suggestions please let me know. Feel free to apply these methods to verify its accuracy

 Article by Sreekar Maddala:

Sreekar Maddala is a Third year Student studying at College of Engineering, Pune. Interested in Financial aspects of City development and planning.