Fintech startup HashChing is an online marketplace for borrowers and mortgage brokers

- March 17, 2016 4 MIN READ

Buying a house is often one of the most important and expensive decisions someone will make. As such, you want the best expert advice that is not formed from biased opinions, but it can be hard to find. Banks are biased towards their own products and are restricted when it comes to operating hours. While it’s often hard to negotiate with banks to get a good rate on a home loan, the alternative is finding a broker, who can offer more choice and customer convenience. However, the trust factor again comes into the equation, with high fees and commissions pushed by brokers fast deterrents for the young buyer.

Borrowers are looking for more choice and personalised answers to their particular situation that the financial experts, constrained by partnerships and the lure of commissions, sometimes can’t offer. When it comes to money and negotiation people want transparency and on-demand services, which is why fintech is such a threat to traditional banks and brokers.

Fintech startup HashChing is an online marketplace for borrowers to access pre-negotiated home loan deals from verified mortgage brokers. The Sydney startup connects consumers instantly to their local mortgage broker based on their postcode and deal preference. The platform then sends the borrower a profile of each local mortgage broker with ratings and reviews collected by HashChing.

“While 54 percent of Australians now trust mortgage brokers for home loans, finding a recommended local mortgage broker with ratings is a challenge as there is no other platform doing this currently,” said Mandeep Sodhi, cofounder of HashChing.“In this on-demand economy, consumers are used to services delivered in minutes and expect a similar experience in mortgages.”

The startup was founded by Sodhi and Atul Narang. Sodhi has experience in financial services and Narang has over 15 years of technology implementation and digital marketing experience. Both founders left their jobs in 2014 to pursue their idea of HashChing and entered the AWI and H2 fintech accelerator where they incubated and developed their idea.

“Home loan is a very competitive space, with most consumers going to comparison sites to compare the rates advertised by different lenders, saving consumers hours of going to individual lender websites to find the rate and more transparency on advertised rates,” said Sodhi.

“HashChing differentiates itself by showing pre-negotiated home loan deals from different lenders that are the same home loan products with the same features advertised on the lender website but with a better rate.”

The pair have built a custom API to show deals by suburb and a dashboard to track each lead, giving customers full visibility of the supply chain, including the mortgage broker they are dealing with.

Once a user has submitted their details on the platform and have chosen a broker based upon comparison, reviews and rates a notification is sent to them with further details of their broker. This includes the broker’s profile picture, phone number, email address, and reviews from previous users. A calendar invite is automatically sent to both consumer and broker, which provides the date and time of their confirmed appointment. Consumers then proceed to upload the required documents on HashChing’s portal and will receive a call from the mortgage broker within 24 hours.

“HashChing is the only platform that works with mortgage brokers from any mortgage aggregator group, which means borrowers get to see negotiated home loan deals from different mortgage groups on one platform. This provides more choice and savings to borrowers with an added advantage of getting connected to a broker instantly who calls the borrower within 24 hours instead of days that lenders usually take,” explained Sodhi.

HashChing doesn’t charge consumers a fee for use, and mortgage brokers only pay for loan settlement and not leads, which is a much better alternative and incentive for brokers than the pay by lead alternative, where money is largely wasted on bad quality leads.

The startup is also focusing on content to better serve users. It allows consumers to ask any home loan related question, which is answered by verified on-site experts. Unlike other comparative websites HashChing has additional services including blog posts and question and answer sections to help borrowers make informed decisions.

The platform initially launched in August 2015 with a few brokers in every state. Now the platform has significantly grown and has verified partner brokers around Australia who work with the four big banks as well as a host of other financial service providers.

In December 2015 HashChing announced a partnership with ASX-listed property group OnTheHouse. The startup has also received investment from AWI Ventures, the H2 accelerator program, and SG Capital.

Sodhi said that the next phase of growth will see the startup acquire customers through more partnerships and the providing of more efficient tools to mortgage brokers to solve the current pain points, from receiving a lead to lodging a home loan application.

While Sodhi and his team have identified other markets where mortgage brokers have a strong presence and trust with borrowers, it is still too early for the startup to expand globally, he said. However, with over 60 percent of home loans in the UK written by mortgage broker instead of lenders directly. With the UK being the biggest and friendliest market for fintech, expansion to the country would be a likely future endeavor for the startup.

Sodhi said, “We are also looking at ways to help mortgage brokers keep their consumers engaged with customised content. Through the data and broker-customer interactions on our custom CRM we have analysed from 700+ home loan applications received in last 7 months, we are now looking at how to better target different segment of consumers with different home loan needs. We have some big announcements to make next month that will help us achieve significant growth.”

Image: Atul Narang and Mandeep Sodhi. Source: Supplied

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