Koichi
Kamijo, a scientist at IBM’s research lab in
Tokyo was only 20 questions into a 300-question life insurance form. And he was
already exhausted. There had to be an easier way to answer these questions. So,
he got together with his colleagues, Ryo
Kawahara, Takayuki
Osogami, Masaki Ono and Shunichi Amano to come up
with a cognitive tech answer to the cumbersome process of finding a life
insurance product. And they identified two critical issues with the life
insurance questionnaire that needed to be solved to make life plan simulator
more user-friendly and useful for consumers and financial planners.
|
L-R: Masaki Ono, Ryo Kawahara, Koichi Kamijo, Takayuki Osogami and Shunichi
Amano of IBM Research – Tokyo |
Typically,
consumers are asked to answer an average of 300 questions, from name, age, and
marital status, to expected salary at the age of retirement, and education
expenses for their children. The first issue Koichi and his team recognized: there
were too many questions! Going through hundreds of questions is too much for people,
even though they help financial planners understand their needs. The second
issue he and his team realized they needed to solve was how to help financial
planners also predict potential needs. With his colleagues’ expertise in
machine learning, business analytics and natural language processing – along
with ST-Life (Smile and Thanks Life), whose life plan simulator Dynamic Life
Plan Navigator has been widely used by enterprises and organizations in Japan –
they developed an insurance information lifecycle management platform to solve these
critical issues.
First, Koichi and his
team combined two components on the Dynamic Life Plan Navigator: its parameter
estimator and human response simulator. The parameter estimator minimized the
burden of answering 300 questions down to only eight by applying machine
learning and analytics technologies. Just by answering questions like marital
status, gender, number of children, income level, date of birth, and home mortgage
status, the system then analyzes other users’ answers to automatically fill in answers
to the other 292 questions. According to the recent experiment, people who
successfully went through life plan simulation questions nearly doubled.
Next, the human
simulator can help discover a user’s potential preferences, such as insurance
premium, coverage type, or what they consider important in choosing a product.
The estimator creates several unconventional, two-option hypothetical questions
based on the user’s data from the past. Then the simulator suggests an option. By
repeating this process four to five times, the simulation system uncovers the user’s
potential preferences, including the ones which the user has never imagined,
such as job, partner and vacation destination preferences.
More
than 97 percent of the users who completed these hypothetical questions in the
recent experiment did not feel it was a burden. And felt that they received an
appropriate life insurance product recommendation.
Protecting
Your Life (Insurance) Choices
Additionally,
the research team created a bonus technology to help avoid posting
inappropriate expressions and private information to online life
insurance-related forums. In Japan, some users want to reference and reply to “people-like-me”
comments in these forums that are often managed by volunteer life insurance
agents and financial advisors. But to post a question can be a challenge
because it can be difficult to determine what information to contribute, while not
disclosing private information.
The
context-aware discussion forum technology the team built highlights potentially
inappropriate expressions and private information to help users amend their
posts before uploading them to a forum. Koichi and his team hope that it will also
help prevent “flaming” (hostile interactions) in the forum due to improper
comments.
Try,
try again
Koichi
used the tool again to do his life plan, and this time, it only took him five
minutes to answer eight questions, overwrite some past answers, and receive a detailed
life insurance plan and product based on future income and stock market
performance.
Koichi
and his team are now working on applying the machine learning platform to other
industries, such as banks and education. For example, like life insurance
products, banks may consider using this platform to offer products to their
customers that suit their savings goals. In education, such a platform may be
applied to help teachers understand their students’ preferences and abilities
by asking questions of their parents. If teachers can anticipate each student’s
preference and ability based on an estimated value the platform’s analytics
technology provides, they may be able to come up with a customized curriculum that
effectively provides student support.
ST-Life
continues to work with Koichi and his team to advance the technology. At IBM
Japan, Koichi and his team plan to collaborate with IBM’s consulting unit to
deploy the insurance information lifecycle management platform to the insurance
industry.
Labels: big data, cognitive computing, ibm research tokyo, natural language processing