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.