Introduction
Amazon Sage Maker and SAP HANA together provide a powerful set of tools that could be used to help protect investors, lenders and borrowers in financial services industry.
Background
There are many risks in the financial services industry, not only when loans fall into arrears and 'default', but from money laundering activities.
First the investors backing the loans, also known as 'funders', are at risk that they will not receive the return they had expected. These investors could be individuals, superannuation funds or banks themselves, ultimately increased costs affect returns and in turn this affects earnings or retirement incomes for ordinary people.
Additionally lenders, such as banks or other financial institutions, who do not detect money laundering by borrowers have reputational risk if their good name is damaged. Depending on legislation in the country in question, there may be fines for non-compliance which will also increase their costs and lower their profit.
Poor performing loans and issues with money laundering could also affect their ability to attract funders to invest on loans in the future. The lender may need to increase interest rates or charges to offset risk and that in turn could make their loan 'product' less attractive to borrowers.
Then there is the risk to borrowers with great potential losses of money from forced sale of the car, house or other investment purchased from the loan (in addition to the very house or car itself) if their loan falls into arrears and defaults. Early intervention with appropriate financial advice could help prevent some of these personal losses.
Finally, money laundering is a community risk when left unchecked that allows criminals to flourish and enables their ability to fund drug mules, people smuggling, illegal gambling or other anti-social activities.
Reducing Risk of Arrears and Defaults
Risk reduction can be achieved by analyzing actual payment amounts and dates versus required payment amounts and dates it should be possible to create a metrics to assess the risk of arrears and defaults as well as detecting money laundering.
During the period between settlement of the loan and discharge of the loan, while the loan is being 'serviced', regular payments will be expected that are consistent in both amount and payment date.
When a borrower doesn't pay on time, the loan can fall into 'arrears'. In some instances, this might be simply a banking error, as one bank fails to transfer funds over a public holiday, or payments from a company payroll account are made too late in the day. These late payments should be flagged to indicate the reason was beyond the control of the borrower.
Late payments made as a result of insufficient funds, a 'bounced' check, or other borrower related issues should be flagged to indicate their source.
These flags in conjunction with the expected payment date and amount, and actual payment date and amount should provide sufficient source data to analyze risk of arrears and defaults across loan in a given portfolio.
The section of the bank or financial organization responsible for servicing the loans can then provide reports of expected arrears situations, preferably on a daily basis, that could help mitigate risk of arrears or defaults.
Detection of Money Laundering
Detection of money laundering activities should be a similar process to detection of arrears and defaults, however special attention needs to be made to additional payments extra to the required payment against a loan, to large payments above a given threshold (where government legislation could set maximum amounts above which reports need to be made), and payments to or from multiple bank accounts.
Without detailing money laundering techniques too closely, as I do not want this to be a guide to help people do money laundering, there are other places that should to be monitored. Especially if loans use 'offset' or 'redraw' facilities, such as might allow transfer of funds between loan accounts, linked or bank accounts or linked credit cards. Additional payments, transfers and withdrawals across these accounts may need to be monitored as a group to prevent risk that money laundering is not discovered.
Points of Interest
Amazon SageMaker makes data analysis and training for SAP HANA very easy. Having the services cloud based and available on-demand can save much in infrastructure costs as you have no hardware or power fees to worry about.
Lenders or loan servicing companies wishing to investigate analysis of data may need to check their legislation, some countries require that the servers are located in that particular country, so that data does not go offshore.
To help mitigate risks of data breach, ensure a unique key is generated for a particular client and that key is used to link data on AWS so that no personal data is sent to the data center during analysis.
History
- 30th October, 2019: Initial version
