Appendix 1: NoFlake Business Case: TechnoTec has decided to develop an innovative financial product for consumers, NoFla
Posted: Fri May 20, 2022 9:29 am
Appendix 1: NoFlake Business Case: TechnoTec
has decided to develop an innovative financial product for
consumers, NoFlake. The management has understood that borrowers
who are at greater risk of having money stolen from them by hackers
(or other forms of cyberattacks) are less likely to be able to
re-pay their debts and are more likely to be a vector for
infiltration of TechnoTec’s IT systems. Therefore, to create a
competitive advantage over the industry leaders, NoFlake will use
an artificial intelligence engine to analyse both the credit risk
and the cyber-security maturity of their borrowers.
How Does NoFlake Work?
The AI engine uses an algorithm (see Appendix 2) to generate a
predictive risk score between -100 and +100 for each potential
borrower (with positive 100 being awarded to thelowest-risk
borrowers and negative 100 being awarded to the highest-risk
borrowers).
Who Built NoFlake?
The algorithm development team consisted of: Philippe
Jankins-Chao (Team Leader, USyd IT graduate, age 26) Carmichael St
Clare (Senior Developer, USyd IT graduate, age 24) Sebastian
Montford-Smythe (Developer, Macquarie IT graduate, age 24) The
development team hired Swizzle Pty Ltd to collect training data for
the algorithm. Swizzle uses individual backpackers (paid minimum
wage) to conduct surveys on major public streets in capital
cities.
Financial Analysis of NoFlake:
TechnoTec currently pays 20 employees an average of $76,000 per
year to determine which loan applicants should be approved, of
which only two employees would be needed after NoFlake is
introduced. NoFlake has two main goals: 1) to outperform human
credit risk analysts by at least 20% (i.e. that the rate of loan
non-repayments by applicants selected by NoFlake will be below 5.6%
vs the current non-repayment rate of 7%); and 2) to lower by at
least 40% (i.e. $600,000) the annual losses incurred by TechnoTec
as a result of cyber-attackers gaining access to its internal
systems using the legitimate log-in details of its customers. In
2020, TechnoTec lost $1.5m from such cyberattacks. On a loan-book
totalling $50 million, the expected annual savings from NoFlake
are: $50,000,000 * 0.014 (credit benefits) + $600,000
(cyber-security benefits) + $76,000 * 18 (labour cost savings) =
$2,668,000. Given an expected cost of development of $1 200 000 and
ongoing operational costs of $300,000, over a five-year period, the
expected savings from implementing NoFlake are: $2,668,000 * 5 –
($1,200,000 + ( $300,000 *5)) = $10,640,000. This equates to an
887% return on initial development costs, or 177% per annum over
five years. Additional revenues could be generated by licencing the
algorithm to third-party financial institutions (such as mortgage
brokers) and paying a trailing commission to those brokers based on
the value of the loans they originate which are approved by the
algorithm. Those brokers would need to be trained in how to gather
and fill in the information needed from borrowers to enable the
algorithm to make its decisions.
Question:
Using the following appendixes give an explanation of how
technology can help decision making (informed by the appendix
above and real-world examples) explaining positive and
negative aspects of the ethical challenges this can create.
has decided to develop an innovative financial product for
consumers, NoFlake. The management has understood that borrowers
who are at greater risk of having money stolen from them by hackers
(or other forms of cyberattacks) are less likely to be able to
re-pay their debts and are more likely to be a vector for
infiltration of TechnoTec’s IT systems. Therefore, to create a
competitive advantage over the industry leaders, NoFlake will use
an artificial intelligence engine to analyse both the credit risk
and the cyber-security maturity of their borrowers.
How Does NoFlake Work?
The AI engine uses an algorithm (see Appendix 2) to generate a
predictive risk score between -100 and +100 for each potential
borrower (with positive 100 being awarded to thelowest-risk
borrowers and negative 100 being awarded to the highest-risk
borrowers).
Who Built NoFlake?
The algorithm development team consisted of: Philippe
Jankins-Chao (Team Leader, USyd IT graduate, age 26) Carmichael St
Clare (Senior Developer, USyd IT graduate, age 24) Sebastian
Montford-Smythe (Developer, Macquarie IT graduate, age 24) The
development team hired Swizzle Pty Ltd to collect training data for
the algorithm. Swizzle uses individual backpackers (paid minimum
wage) to conduct surveys on major public streets in capital
cities.
Financial Analysis of NoFlake:
TechnoTec currently pays 20 employees an average of $76,000 per
year to determine which loan applicants should be approved, of
which only two employees would be needed after NoFlake is
introduced. NoFlake has two main goals: 1) to outperform human
credit risk analysts by at least 20% (i.e. that the rate of loan
non-repayments by applicants selected by NoFlake will be below 5.6%
vs the current non-repayment rate of 7%); and 2) to lower by at
least 40% (i.e. $600,000) the annual losses incurred by TechnoTec
as a result of cyber-attackers gaining access to its internal
systems using the legitimate log-in details of its customers. In
2020, TechnoTec lost $1.5m from such cyberattacks. On a loan-book
totalling $50 million, the expected annual savings from NoFlake
are: $50,000,000 * 0.014 (credit benefits) + $600,000
(cyber-security benefits) + $76,000 * 18 (labour cost savings) =
$2,668,000. Given an expected cost of development of $1 200 000 and
ongoing operational costs of $300,000, over a five-year period, the
expected savings from implementing NoFlake are: $2,668,000 * 5 –
($1,200,000 + ( $300,000 *5)) = $10,640,000. This equates to an
887% return on initial development costs, or 177% per annum over
five years. Additional revenues could be generated by licencing the
algorithm to third-party financial institutions (such as mortgage
brokers) and paying a trailing commission to those brokers based on
the value of the loans they originate which are approved by the
algorithm. Those brokers would need to be trained in how to gather
and fill in the information needed from borrowers to enable the
algorithm to make its decisions.
Question:
Using the following appendixes give an explanation of how
technology can help decision making (informed by the appendix
above and real-world examples) explaining positive and
negative aspects of the ethical challenges this can create.