
Explainable Decisions-as-a-Service for Retail and Insurance

It is no secret that artificial intelligence (AI) is transforming numerous industries, providing organizations with a competitive edge and enabling them to outperform their competition.
In particular, AI that delivers explainable decisions is
rapidly driving companies to success. Delivering
explainable decisions builds trust in AI-powered
recommendations and ensures the transition to autonomous
operation is successful - allowing organizations to fully
reap the benefits AI offers.
This blog post
will provide a comprehensive introduction to AI that
delivers explainable decisions, defining the concept, how
it compares to traditional tools, the benefits it offers,
and how the AI is being applied in the retail and
insurance spaces.
What Are Explainable Decisions?
Explainable decisions are AI-powered decisions that are
delivered with context - allowing the personnel using the
AI system to fully understand the logic behind the
recommendations being provided. Explainable decisions mean
that the AI system is not a black box – there is
visibility into the way the system operates and the basis
for its decisions.
Explainable decisions
leveraged in retail and insurance are used to develop
trust in AI-powered decisions as organizations transition
from using traditional technologies and methods to
automating core processes with AI systems.
Trust is the basis for success in any AI implementation
effort as AI is not a project, it is a significant
transformation initiative and will change the way an
organization operates. It is not in human nature to accept
such significant change with no understanding in the
technology that is taking over day-to-day
responsibilities. As such, explainable decisions are a
necessary component to ensuring the success of AI
implementation efforts.
Explainable AI VS. Traditional Tools
The main point of differentiation between
traditional advanced analytics systems and explainable AI
systems is the level of transparency and visibility. As
aforementioned, AI that delivers explainable decisions
provides full visibility into the internal logic of the
system – providing users with the rationale behind all
outputs and decisions.
However, the same
cannot be said for traditional analytics tools. Many of
these traditional systems are black boxes – only
permitting visibility to the inputs and the outputs of the
system but providing no clear visibility into the
processes in between. Even when end-users are allowed a
look into the inner workings of the system, they are
typically unable to gain any deep understanding due their
lack of expertise with the technology.
The Benefits of Explainable Decisions
In addition to the emphasis placed on providing a clear rationale for all recommendations, AI that delivers explainable decisions comes with several other benefits.
Most notably, these systems require no code, infrastructure, or data scientist personnel and these systems can integrate into existing back-end processes, such as robotic process automation (RPA) systems in the case of insurance. These systems will also automate and continually process the most up-to-date data as they are extremely dynamic - operating and learning faster than the pace of time.
Furthermore, building trust
in AI technology with explainable decisions has the power
to elevate the role of people in the workplace. When
trust is built and the AI system is allowed to take on the
mundane tasks it is far better suited to do, workplace
personnel see significant time savings. This time will
ultimately be reinvested and will be used take on the high
value tasks only they do best – this includes
business strategy and meeting customers’ needs.
Explainable Decisions in Retail

In retail, an explainable decision may look like a rationale for
why a certain price point was recommended. For example,
suppose the AI system recommends that ground beef is
promoted at an unexpectedly low price. With no
explanation, this recommendation may appear to be a
mistake – making retailers wary to accept and action the
recommendation.
However, when the decision is
explained (the low price point will increase customer
traffic and subsequently drive the sales of products
related to ground beef, such as pasta and tomato sauce),
it becomes clear that the decision was well justified –
the retailer will recoup the margin loss on the ground
beef with the associated sales of the other products. With
each explanation, retailers will reach a greater
understanding and trust that the thousands of decisions
across their store are being made correctly.
A typical retail store houses thousands of products – resulting in millions of possible item and price combinations. Making decisions of this magnitude for all products across an entire store is a task that is a measure beyond human ability. Building trust in AI means these decision-making tasks can be turned over to the system, alleviating the strain on the retailers making these impossible decisions.
Explainable Decisions in Insurance

In insurance, an explainable decision may look like a rationale for
why a claim was denied due to fraud. Suppose a claim for
prescription medication was deemed fraudulent by the AI
system. With no further explanation, it may seem incorrect
that this claim was flagged as fraud – there is nothing
inherently suspicious about a low value claim for
medication. What’s more, insurers that are not leveraging
AI systems might even process this claim, as it is not
cost effective to investigate further.
However, when the logic behind the AI’s
decision is revealed, it becomes clear that it was well
justified. In this case, the decision was founded on the
fact that the prescribing doctor is writing prescriptions
too frequently in comparison to multiple sets of peers
(such as similar specialty doctors, doctors with practices
in the same area, or doctors prescribing to the same age
of patients at the same time of year). Behaviour that is
anomalous compared to multiple peer groups makes the case
for fraud and abuse clear. Once again, with each
explanation, insurers begin to feel confident and trust
that the decisions across their organization are being
effectively handled by the AI.
AI has rapidly
reached every industry and is becoming the key strategy
for success among retailers and insurers today. As such,
organizations looking to compete and lead their industry
are quickly investing in AI technology and implementing it
internally to automate core processes.
Leveraging AI that delivers explainable decisions is a sure-fire way to ensure AI implementation efforts are successful. Explainable decisions work to establish trust in AI-powered recommendations – making the technology far easier to accept. AI that delivers explainable decisions is also incredibly dynamic, outperforming traditional tools and elevating the role of people in the workplace. The end result is time savings of 40% and a ROI of >10X.
Ultimately, implementing AI is a significant transformation effort. As such, it is important end-users understand and believe in the technology they are using. The transparency explainable decisions provide will be the driving force behind long term success – ensuring retailers and insurers can continue to compete and provide customer value.
Learn more about the technology that is enabling Daisy to provide value in the retail and insurance spaces. Get in touch.
