The classical approach of targeting clients on the loan
market is to sell the loan product based on communicated parameters. Banks
display features of the products as a primary acquisition driver. They usually
compete in speed of the approval process, interest rates, flexible duration.
However, the world around us and customer needs are changing so fast that
keeping up with the pace is challenging. A proven value proposition will not be
helpful in the next six months.
Prospective clients bombarded from every corner by new value
offerings are becoming reluctant to any unique product proposition. It is
becoming harder and harder to address a client with a need to take a loan.
Furthermore, this is where banks compete these days: keeping clients inside
their product ecosystem.
Rising competition squeezes the product margin, and churn is
not helping the profitability. It is challenging to acquire a client with just
product parameter proposals as clients are becoming more sophisticated. The
ability to compare parameters of a product makes client behavior more volatile
and harder to predict.
The old value proposition offering based on the buyer’s
persona approach is becoming less relevant. Borrowers do not want to get a loan
product. What they need is to solve their particular problem. They have to have
a reason to take a loan to purchase any goods, property, or service. Clients
expect to have a tailor-made value proposition based on particular needs. What is today considered to be an excellent
approach to solving a client’s problem will not be probably valid in six
months.
Atomization of the client problems is forcing banks to
address the change in value proposition radically. An excellent approach to
attract a client is not to sell a product but a solution to his/her needs. The
hyper-personalization approach to offer a loan is becoming a new trend in value
offerings. Value proposition based on the custom-made reasoning why to use
product is much more relevant and understandable to a client than just
promoting parameters of the product.
Hyper - personalization offering approach provides a client
with a perfect-fit solution based on:
• Personal preferences based on former purchasing behavior
• Risk profile based on loan registers
• Life goals
• Family status and financial health of family members
• Economical surroundings and expected development,
• Behavior of peers on the market, etc.
By automated hyper-personalization offering, the client gets
a tool that helps him to reach his goals.
Hyper-personalization requires a change in the mindset and
cultural behavior of financial institutions. The best way is to turn into a
continuous-decisioning approach. It is a repeating cycle of an idea, test,
learn, and try to get as much as possible out of the data. Then swiftly make
and implement decisions. The problem is that this cycle is turning very fast.
And banks are, by nature, still not very well flexible as to the change of
those parameters. Challenges are both in the people management and legacy
systems deployed.
FinTech can address these challenges by entirely focusing on
the topic. FinTech companies are usually more flexible in the try-error- learn-implement
cycle. Researching machine learning, efficient data structures, a fast learning
curve, and experience across the industry keep them more versatile. Connecting
the banking data structures with external data such as public registers is
usually easier for FinTech companies as governments provide support via
incubators and legal incentives. Financial institutions can leverage FinTech
skills while keeping the risk of failure outside their structures and balance
sheets. It seems that this field can bring only win-win situations for FinTech
sector and financial institutions in the near future.
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