Do you want to know “how banks rate you when you borrow money from banks”?


Hi friends,  I am Toshi, This is my weekly letter. This week’s topic is “how banks rate you when you borrow money from banks”. When we want bank loans, it is good that we can borrow the amount of money we need,  with a lower interest.  Then I am wondering how banks decide who can borrow the amount of money requested with lower interests. In other words, how banks assess customer’s credit worthiness.  The answer is “Classification”.  Let me explain more details. To make the story simple,  I take an example of  unsecured loans, loans without collateral.


1.  “Credit risk model” makes judgements to lend

Now many banks prepare their own risk models to assess credit worthiness of customers.  Especially global banks are required to prepare the models by regulators, such as BIS, FSA and central banks. Major regional banks are also promoted to have risk models to assess credit worthiness.  Regulations may differ from countries to countries,  by size of banks.  But it is generally said that banks should have their risk models to enhance credit risk management.  When I used to be a credit risk manager of the Japanese consumer finance company, which is one of  the group companies in the biggest financial group in Japan,  each customer is rated by credit risk models. Good rating means you can borrow money with lower interest. On the other hand, bad rating means you can borrow only limited amount of money with higher interest rate or may be rejected to borrow. From the standpoint of management of banks, it is good because banks can keep consistency of the lending judgements to customers among the all branches.  The less human judgement exists, the more consistency banks keep.  Even though business models may be different according to strategies of banks, the basic idea of the assessment of credit worthiness is the same.


2. “Loan application form” is a starting point of the rating process

So you understand credit risk models play an important role. Next, you may wonder how rating of each customer is provided.  Here “classification” works. Let me explain about this.  When we try to borrow money,  It is required to fill “application forms”. Even though the details of forms are different according to banks,  we are usually asked to fill “age” “job title” “industry” “company name” “annual income” “owned assets and liabilities” and so on.   These data are input into risk models as “features”.   So each customer has a different value of “features”.  For example, someone’s income is high while others income is low.   Then I can say  “Features”of each customer can explain credit worthiness of each customer.   In other words,  credit risk model can “classify”  customers with high credit worthiness and customers with low credit worthiness by using  “features”.


3.  Rating of each customer are provided based on “probability of default

Then let us see how models can classify customers in more details. Each customer has values of “features”  in the application form. Based on the values of “features”, each customer obtains his/her own “one value”.  For example, Tom obtains “-4.9” and Susum obtains “0.9” by adding “features” multiplied with “its weight”.  Then we can obtain “probability of default” for each customer.  “Probability of default” means the likelihood where the customer will be in default in certain period, such as one year. Let us see Tom’s case. According to the graph below,  Tom’s probability of default, which is shown in y-axis, is close to 0.  Tom has a low “probability of default”. It means that he is less likely to be in default in the near term. In such a case,  banks provide a good rating to Tom. This curve below is called “logistic curve” which I explained last week. Please look at my week letter on 23 April.


Let us see Susumu’s case. According to the graph below,  Susumu’s probability of default, which is shown in y-axis, is around 0.7, 70%.  Susumu has a high probability of default. It means that he is likely to be in default in the near term. In such a case,  banks provide a bad rating to Susumu. In summary,  the lower probability of default is,  the better rating is provided to customers.



Although there are other methods  of “classification”,  logistic curve is widely used in the financial industry as far as I know. In theory, the probability of default can be obtained for many customers from individuals to big company and sovereigns, such as “Greeks”.  In practice, however, more data are available in loans to individuals and small and medium size enterprises (SME) than loans to big companies.  The more data are available, the more accurately banks can assess credit worthiness. If there are few data about defaults of customers in the past,  it is difficult to develop credit risk models effectively. Therefore, risk models of individuals and SMEs might be easier than risk models of big companies as more data are usually available in loans to individuals and SMEs.

I hope you can understand the process to rate customers in banks. Data can explain our credit worthiness, maybe better than we do. Data about us is very important when we try to borrow money from banks.

Can Abenomics achieve its objects after winning the election?


Liberal Democratic party (LDP) won the election in Japan yesterday. Then Abenomics is going to fight against financial markets.  Financial market is tougher than other political parties as it reacts very quickly. If LDP can not convince the financial market that financial condition of Japan can be improved,  interest rate of Japanese government bond (JGB) will increase rapidly and price of JGB will be plunged. It means collapse of Japanese fiscal condition as Japanese government might not repay interest rare of debts. This is the race against time.

Moody’s Investors Service downgraded the Government of Japan’s debt rating by one notch to A1 from Aa3 at 1st December 2014.  The outlook is stable. This is the beginning of the story.  From now on,  market participants will focus on how Abenomics work in Japan after LDP won the election.  Rise of consumption tax is postponed until April 2017.  So only less than two and half years are left for Japan. Can Abenomics turn deflation to inflation during such a short period?  Can productivity of Japan be improved strong enough to rise the consumption tax?

Debt to GDP ratio already exceeds 200%.  Japan has no experience of such heavy burden except the time after world war 2. Ray Dalio, founder of Bridgewater Associates, explains that we need policy mix below to solve the heavy burden of national debt .

1. Wealth redistribution

2. Spending cut

3. Debt restructuring

4. Debt Monetization

In short,  tax rate will be  increased to wealthy people,  Japanese capital expenditure and social welfare will be cut and Bank of Japan will finance JGB.   This plocy mix is needed to avoid JGB default. LDP should convince Japanese people that this policy mix is needed and should be exercised in a timely manner.  This is the toughest task for LDP.  But it can not be postponed because it allows financial market to trigger the interest hike.

Productivity may increase gradually by Abenomics however it can not offset hike of consumption tax rate.  At the digital era,  knowledge of software engineering,  machine learning and artificial intelligence are critically important.  These are key in order to optimize the systems,  streamline the processes and improve the productivity. Unfortunately management of Japanese big companies are not familiar with fields of software . So Japanese companies are generally shy and away the innovations to improve productivity by big change.  I do see a few managements whose majors are software engineering and compter science in Japan.  In my view,  it is too late to change this situations because it takes ten years to train new managements within companies.  But we cannot wait such a long period anymore.

Anyway,  we should listen carefully to what the financial  market says.  I am not sure whether LDP wins over the financial market or not. All I can say is that Japan should be changed by herself, rather than being forced to change  by the market.

Credit Risk Management and Machine Learning in future


Credit risk is always s hot topic in economic news.   Are US Treasury bonds safe? Does the Chinese banking system have problems?   Is the ratio of household debt to GDP is high in Malaysia?  etc. When we think about credit risk, it is critically important to know how we measure credit risk. So I would like to reconsider how we can apply Machine learning to credit risk management in financial institutions.  When I used to be a credit risk manager in a Japanese consumer finance company 10 years ago,  there were no cloud service and no social media. However, now that it is the time of big data,  it is a good timing to reconsider how we can manage credit risk by using the latest technologies such as cloud services and Machine learning. Let me take three points as follows.


1. Statistical models

One of the key metrics in credit risk management is the probability of  defaults (PD).  It is usually calculated from statistical models such as regression analysis.  Machine learning has algorithms about regression analysis, therefore it must  be easy to implement Machine learning to credit risk management systems in financial intuitions. Once PD is calculated from Machine learning,  this figure can be used the same way as current practices in financial institutions.  Statistical models usually have versions since it is developed but there is no need to worry about version control because it is easy on the cloud system.


2. Data

Data is more different from one which used to be 10 years ago.   I used private and closed data only in order to calculate the risk for each borrower.  Now that social media data and open public data are available in risk management,  we need to consider how to use these kind of data in practice. For this purpose,  cloud services are good because they are scalable and easy to expand the capacity to store data whenever it is needed.


3.  Product development

Recently product development is fast and active to keep the competitive edge as customer can choose channels, to contact with financial institutions and are getting more demanding.  That is why risk management in financial institutions should be flexible to update product portfolio and adjust methods in the light of  characteristics of new products.  Combination of cloud service and machine learning enable us to develop the risk models, enough to quick to keep up with new products and change of business environments.


Unlike retail industries,  financial industries are heavily regulated and required to audit risk management systems periodically. Therefore, audit trails are also important when Machine learning is applied to credit risk management.  I think combination cloud services and machine learning is good to enhance credit risk management with cost-effectiveness  in the long run. I would like to try this combination in credit risk management  if I can do it.