What is the primary value proposition of BorrowBot?

BorrowBot positions itself as an automated personal loan advisor. According to Slide 2 and Slide 4, the value lies in using a chatbot interface to simplify the loan discovery process. It addresses the problem that borrowers lack the information to identify the 'best' loan among various financial institutions, specifically targeting mortgages, auto loans, and SME lending.

How does BorrowBot plan to generate revenue?

Slide 8 outlines five distinct revenue streams: Referral Fees, Lead Sales, Licensing to Institutes, Featured Ads, and Commissions. This suggests a B2B2C model where the company monetizes both the user's intent (leads/referrals) and the financial institution's need for technology (licensing) or visibility (ads).

What market size does the deck claim for Thailand?

Slide 6 focuses exclusively on the mortgage market in Thailand. It provides a historical growth chart showing the market rising from 2,309,998 million (2.3 trillion) baht in 2013 to 3,071,077 million (3.07 trillion) baht in 2016. This is used to justify the 'Large Mortgage Market' opportunity.

Who are the founders and what are their roles?

Slide 9 introduces the 'Squad' consisting of four members: Woraphop (Business Developer), Kimseng (Customer Developer), Kimsie (Creative Designer), and Cholathit (Product Developer). However, the deck does not provide any professional background, previous experience, or education for these individuals, which is a significant omission for investors.

What are the biggest weaknesses in this pitch deck?

The deck lacks three critical components: Traction, Competition, and the Ask. There are no figures regarding current user numbers or revenue. It does not list any competitors, which is unrealistic in the fintech space. Finally, it fails to state how much money the company is seeking or how that money will be spent.

BorrowBot Pitch Deck (2016): 12-Slide Seed Deck

See all 12 slides of the BorrowBot pitch deck — a 2016 Early / Seed deck — with a slide-by-slide teardown of what the deck does well and where it falls short.

BorrowBot’s 2016 pitch deck presents a solution for the information asymmetry in the Thai lending market. By utilizing a chatbot interface, the company aimed to guide users through the complexities of mortgages, auto loans, and SME financing. The deck highlights a significant market opportunity, citing a mortgage market value of 3.07 trillion baht in 2016. While the presentation is strong on vision and market size, it lacks critical financial data, such as specific unit economics, current traction metrics, or a defined funding ask. The execution plan relies heavily on partnerships and social…

Key takeaways

The company identifies a specific pain point: borrowers do not know which financial institutions offer the best loans (Slide 3).
The product is positioned as an automated chatbot system that acts as a personal loan advisor (Slide 2 and Slide 4).
BorrowBot targeted a mortgage market in Thailand that grew from 2.3 trillion baht in 2013 to over 3.07 trillion baht in 2016 (Slide 6).
The initial target audience is defined as Bangkok residents aged 20-50 who are smartphone users and have profitable careers (Slide 7).
Revenue streams are diversified across referral fees, lead sales, licensing to institutes, featured ads, and commissions (Slide 8).
The execution plan indicates that the prototype phase was completed prior to the deck's publication (Slide 10).
Future milestones included expansion into Cambodia, Vietnam, and Myanmar by 2018 (Slide 11).
The deck completely omits a specific fundraising 'Ask' or a breakdown of how capital would be allocated.

BorrowBot Pitch Deck Analysis

The BorrowBot deck from 2016 is a classic example of early-stage fintech positioning. It leans heavily on the then-emerging trend of conversational UI (chatbots) to solve a traditional financial services problem: lead generation and advisory in the mortgage sector. While the visual presentation is clean, the content is high-level, focusing more on the 'what' and 'where' rather than the 'how' and 'how much.'

Slide 1: Title Slide

The cover slide features the BorrowBot logo—a stylized 'B' inside a chat bubble—and the tagline 'choose wisely, no regrets.' The background image of money being exchanged is literal and sets the stage for a finance-focused presentation. It establishes the brand identity immediately but offers no specific industry context until the following slides.

Slide 2: What is BorrowBot?

This slide provides a concise definition: 'Borrowbot is an automated system which recommends borrowers the right loan via our Chatbot.' It includes a mockup of a smartphone running a messaging app. This is an effective 'high-concept pitch' slide that explains the product in a single sentence. By using the term 'automated system,' the founders are signaling a scalable technology play rather than a manual brokerage service.

Slide 3: The Problem

The problem statement is simple: 'Borrowers don’t know which financial institutions can offer the best loan for them.' The background features various bank buildings in Thailand (such as Bangkok Bank and TMB). This slide identifies information asymmetry as the primary barrier for consumers. However, it lacks data to support how much time or money is currently wasted due to this problem.

Slide 4: The Solution

Slide 4 shows a desktop/web interface mockup of the chatbot in action. The bot greets the user as a 'personal loan advisor.' This slide is intended to show the user experience. It reinforces the conversational nature of the product, suggesting that the complexity of loan comparison is hidden behind a simple chat interface.

Slide 5: Product Demo

This is a transition slide featuring a tablet on a grass background with a 'Product Demo' button. In a live presentation, this would likely trigger a video or a live walkthrough. For a static deck, it serves to inform the reader that a functional product exists, though no actual demo content is visible in the PDF version.

Slide 6: Market Size

The deck focuses on the 'Large Mortgage Market in Thailand.' It provides a line graph showing growth in 'Trillian baht' (likely a typo for Trillion). The figures cited are 2,309,998 million in 2013, 2,783,129 million in 2014, 2,950,835 million in 2015, and 3,071,077 million in 2016. This slide successfully demonstrates a large, growing TAM (Total Addressable Market) in the company's home country.

Slide 7: Starting Target

BorrowBot defines its initial target as Bangkok residents, male and female, aged 20-50. They specify that these users should have 'profitable careers' (employed or SME owners), be smartphone users (Android/iOS), and have a need for finances (mortgages, auto loans, SME loans, personal loans). This is a broad but logical starting point, focusing on the urban center where smartphone penetration and financial literacy are highest.

Slide 8: Revenue Stream

The business model is diversified into five categories: Referral Fees , Leads sales , Licensing to Institute , Featured Ads , and Commission . While this shows multiple ways to make money, it can also be a red flag for investors who prefer a focused primary revenue driver in the early stages. The slide does not explain the percentage or dollar value of these fees.

Slide 9: The Team

The 'Squad' slide introduces four members: Woraphop, Kimseng, Kimsie, and Cholathit. They are assigned roles in business development, customer development, creative design, and product development. The major flaw here is the total lack of credentials. There are no logos of former employers, no university names, and no specific achievements listed for any team member.

Slide 10: Execution Plan

This slide uses a roadmap graphic to show four steps: 01 Prototype (marked as 'Done'), 02 User Acquisition (via money expos, office areas, property developers, and social media), 03 Tweak & Optimize (based on feedback), and 04 Market Expansion (partnerships and mass marketing). It shows a basic understanding of the startup lifecycle but lacks specific dates or KPIs for each phase.

Slide 11: Milestones

The timeline extends from 2016 to 2019. 2016 was for 'Finding the Right Model.' 2017 was 'Take off the Ground' (conquering the Bangkok market). 2018 was 'Get Outside of the Box' (expanding to Cambodia, Vietnam, and Myanmar). 2019 was 'Conquer ASEAN Market.' These are ambitious goals, but the deck provides no evidence of the regulatory or linguistic capabilities required to expand into three different countries within 24 months.

Slide 12: Closing Quote

The deck ends with a quote from Ro May: 'before you make a choice, make sure you can live with it.' This is a thematic closing that ties back to the 'no regrets' tagline, but it replaces the traditional 'Contact Us' or 'Ask' slide, which is a missed opportunity to provide a clear call to action.

What BorrowBot Does Well

The deck is visually consistent and easy to read. It avoids the 'wall of text' trap that many early-stage founders fall into. The problem and solution are linked clearly through the chatbot interface, and the market size slide (Slide 6) uses specific, cited figures to prove the opportunity is worth pursuing. The definition of the target audience (Slide 7) is also more specific than many generic 'everyone with a phone' descriptions found in other decks.

What is Missing from the Deck

The Ask: There is no mention of how much capital the company is raising, the valuation, or the terms of the round. · Traction: Slide 10 says the prototype is 'Done,' but there are no metrics. How many users have chatted with the bot? How many leads have been generated? Even beta test data would be valuable here. · Competition: The deck ignores the existence of other loan comparison sites, traditional brokers, and the banks' own digital efforts. · Unit Economics: While Slide 8 lists revenue streams, it doesn't explain the Cost Per Acquisition (CPA) versus the Lifetime Value (LTV) of a user. · Team Pedigree: Without professional backgrounds, the team slide fails to build investor confidence in the founders' ability to execute in a highly regulated industry like finance.

Lessons for Founders

Founders can learn from BorrowBot's ability to simplify a complex product into a clear narrative. However, the omissions in this deck serve as a cautionary tale. If you are pitching a fintech product, you must address the regulatory environment and the competitive landscape. Furthermore, never leave out the 'Ask'—investors need to know what you want from them. Finally, if you include a team slide, ensure it highlights why your team is uniquely qualified to solve the specific problem you've identified.

Frequently asked questions

What is the primary value proposition of BorrowBot?: BorrowBot positions itself as an automated personal loan advisor. According to Slide 2 and Slide 4, the value lies in using a chatbot interface to simplify the loan discovery process. It addresses the problem that borrowers lack the information to identify the 'best' loan among various financial institutions, specifically targeting mortgages, auto loans, and SME lending.
How does BorrowBot plan to generate revenue?: Slide 8 outlines five distinct revenue streams: Referral Fees, Lead Sales, Licensing to Institutes, Featured Ads, and Commissions. This suggests a B2B2C model where the company monetizes both the user's intent (leads/referrals) and the financial institution's need for technology (licensing) or visibility (ads).
What market size does the deck claim for Thailand?: Slide 6 focuses exclusively on the mortgage market in Thailand. It provides a historical growth chart showing the market rising from 2,309,998 million (2.3 trillion) baht in 2013 to 3,071,077 million (3.07 trillion) baht in 2016. This is used to justify the 'Large Mortgage Market' opportunity.
Who are the founders and what are their roles?: Slide 9 introduces the 'Squad' consisting of four members: Woraphop (Business Developer), Kimseng (Customer Developer), Kimsie (Creative Designer), and Cholathit (Product Developer). However, the deck does not provide any professional background, previous experience, or education for these individuals, which is a significant omission for investors.
What are the biggest weaknesses in this pitch deck?: The deck lacks three critical components: Traction, Competition, and the Ask. There are no figures regarding current user numbers or revenue. It does not list any competitors, which is unrealistic in the fintech space. Finally, it fails to state how much money the company is seeking or how that money will be spent.

Cover slide of the BorrowBot pitch deck — Early Stage / Seed 2016 — BorrowBot pitch deck, slide 1 (2016)

BorrowBot pitch deck: the facts

Company: BorrowBot
Year: 2016
Stage: Early Stage / Seed
Slides: 12
Sector: Fintech / Chatbot
Deck type: Pitch Deck
Headquarters: Bangkok, Thailand

BorrowBot pitch deck PDF

The full BorrowBot deck is embedded on this page and can be read slide by slide in the browser — no download or account required. Each slide is covered in the breakdown above.

What the BorrowBot pitch deck was used for

BorrowBot is a Thailand-based fintech concept presented in a 2016 early-stage/seed pitch deck focused on using an automated chatbot to recommend loans to local borrowers. The deck positions the company as a conversational AI interface to help users access mortgages and other consumer and SME loan products in the Thai market. It specifically targets smartphone users in Bangkok who are active on social platforms and have stable, profitable careers but need financing, especially for real estate. This JSON describes that public pitch deck rather than any verified funding event, as no external funding information could be found beyond the Slideshare upload.

What the BorrowBot deck got right

Well-articulated, digitally savvy urban borrower segment. The deck identifies a clear starting target group—Bangkok residents aged 20–50 who are smartphone users on Android and iOS, active on social networks like Facebook, Line, and Instagram, and who have profitable careers and a need for mortgages, auto, SME, or personal loans—showing thought about both demographics and digital behavior.
Concept aligns with emerging conversational AI and digital lending trends. BorrowBot’s core value proposition of using an automated chatbot to recommend and guide borrowers through mortgage and other loan options in Thailand aligns with broader trends toward conversational interfaces and digital lending, positioning the product concept in a growing fintech niche.
Multi-product loan focus increases platform scope. By explicitly mentioning multiple loan categories—mortgages, auto loans, SME loans, and personal loans—the deck frames BorrowBot as a versatile platform that can serve a range of borrower needs rather than a single-product tool, increasing perceived platform potential.
Clear linkage from chatbot concept to a large mortgage opportunity. The deck narrative connects BorrowBot’s chatbot approach to the large Thai mortgage market, framing a path to capturing share in a multi-trillion baht opportunity and helping investors understand why a loan recommendation chatbot could be financially meaningful if executed well.
Focus on income-stable borrowers attractive for lender partners. Positioning the initial user base as people with profitable careers—either employed or running small and medium businesses—implicitly targets borrowers with income stability, which could be attractive to lenders and supportive of feasible credit quality in lender partnerships.

What could have been stronger

Broad target segment without quantified market validation. The starting target group is defined broadly as Bangkok residents aged 20–50 who are smartphone users on multiple social platforms and have profitable careers, but the deck does not show how large this segment is, how many of them actively seek mortgages or other loans via digital channels, or any quantified customer validation, leaving market focus and validation unclear.
No sourced market or regulatory data in the presentation. Although the deck frames the opportunity around a multi-trillion baht mortgage market and several loan categories, it does not present any sourced market-size data, regulatory overview, or lender-side economics to support the opportunity sizing or feasibility of chatbot-enabled loan origination in Thailand.
Missing detail on lender integration and regulatory compliance. From the available slide text, there is no explanation of how BorrowBot would integrate with banks or non-bank lenders, how it would handle credit assessment, or how it would comply with Thai lending regulations, which is particularly important in a highly regulated loan market.
Lack of demonstrated traction or partnerships. The OCR text and public deck snippet do not show any traction metrics (users, pilots, lender partnerships, or loan volume) at the time of pitching, making it difficult for investors to assess execution progress beyond the concept stage.
Unclear monetization mechanics beyond high-level opportunity framing. The deck text we have does not mention specific monetization mechanics (commission structure, referral fees, revenue share per loan, or subscription payments from lenders), leaving the business model implied but not explicitly articulated.

How an investor would read this deck

What draws attention

Evidence that Bangkok urban borrowers in the 20–50 age range will trust and use a chatbot interface for mortgages and other loans, including any pilots, surveys, or early usage data beyond the concept described in the deck.
Concrete plans and progress on integrating with Thai banks or licensed lenders, including signed partnership agreements, API integrations, and compliance with local lending regulations.
Differentiation versus existing digital lending products and bank-operated chatbots in Thailand, given that larger incumbents later rolled out their own chatbot-based lending flows.
Clarity around how BorrowBot would monetize each loan interaction (e.g., referral fees, commissions, or revenue share) and what unit economics look like per customer segment.

Risks that stand out

Regulatory tightening around digital personal loans and consumer protection in Thailand may restrict how third-party platforms like a loan recommendation chatbot can operate and share borrower data with lenders.
Large Thai banks and licensed digital lending operators can deploy their own chatbots and digital lending flows, potentially eroding BorrowBot’s differentiation and negotiating power with lenders.
If BorrowBot does not secure strong lender partnerships, it may struggle to offer competitive loan options, reducing user trust and making customer acquisition costly despite the broad target segment noted in the deck.
Consumer trust and adoption of chatbot interfaces for high-stakes decisions like mortgages may be slower than anticipated, impairing the company’s ability to convert conversations into loan applications at a sustainable cost.

Questions this deck invites

What specific legal entity, licenses, or regulatory approvals does BorrowBot (or its founders) hold in Thailand to intermediate or recommend loans, and how does the model comply with Bank of Thailand rules on digital lending and personal lo
Which banks, finance companies, or non-bank lenders have formally agreed to work with BorrowBot, and what do those partnership contracts stipulate about data sharing, commissions, and exclusivity?
What empirical data (user tests, pilot funnels, conversion rates) shows that Bangkok borrowers prefer a chatbot interface over traditional channels when deciding on mortgages or other loans?
How does BorrowBot plan to manage credit risk and suitability—does it merely refer leads, or does it participate in underwriting using alternative data as emerging Thai digital lending guidelines encourage?
What is the concrete product roadmap and timeline from prototype chatbot to a compliant, revenue-generating platform integrated with multiple lenders, and what milestones from that roadmap have already been reached?

What founders can take from the BorrowBot deck

A pitch targeting a regulated financial vertical like mortgages needs at least a minimal discussion of regulatory context, licensing, and lender integration to be credible beyond the concept stage.
Defining a target segment in demographic and behavioral terms (Bangkok residents aged 20–50 who use smartphones and social apps and have profitable careers) is useful, but investors also need quantified market size and validation to assess commercial potential.
When a deck claims an opportunity in a multi-trillion baht mortgage market, supporting those claims with sourced data and clear monetization mechanics is critical to avoid appearing purely aspirational.
Early-stage fintech decks benefit from showing tangible traction—pilots, lender commitments, or at least user tests—even if small, to reduce perceived execution and adoption risk.
Conversational AI as a front end for complex financial products is compelling, but a strong pitch must connect that UX layer to the underlying economics and risk management of lending ecosystems.
Public pitch decks that later circulate without accompanying company updates can become historical artifacts; building in clearly labeled assumptions and data sources helps future readers distinguish early-stage hypotheses from verified performance.

BorrowBot pitch deck: common questions

What is BorrowBot according to the 2016 pitch deck?

BorrowBot is described in its 2016 pitch deck as an automated chatbot system designed to recommend loans—including mortgages, auto loans, SME loans, and personal loans—to borrowers in Thailand. It aims to use conversational AI to match consumers with appropriate loan products in the Thai mortgage and consumer credit market.

Who was the initial target customer segment in the BorrowBot deck?

The pitch deck identifies its starting target group as Bangkok residents aged 20–50 who use smartphones (Android and iOS) and social platforms like Facebook, Line, and Instagram, and who have a profitable career either as employees or small/medium business owners and need financing for mortgages, auto loans, SME loans, or personal loans.

What kind of fundraising deck is the BorrowBot Pitch Deck, and when was it created?

The deck is an early-stage/seed fundraising presentation from 2016, publicly available on Slideshare under the title "BorrowBot Pitch Deck," attributed to presenter Cholathit Khueankaew. It appears intended to support an initial seed raise for building and launching the chatbot-based loan recommendation platform in Thailand.

How does the BorrowBot deck say the company plans to make money and grow?

The deck claims BorrowBot will capture a share of Thailand’s multi-trillion baht mortgage market by offering an automated, conversational interface that guides users through loan selection and application, presumably partnering with lenders to originate loans. It positions conversational AI as a way to simplify and personalize the loan discovery process for Thai consumers.

Is there any verified information about BorrowBot’s funding, investors, or later progress?

Beyond the publicly available Slideshare pitch deck and its basic metadata, no credible information could be found about BorrowBot’s incorporation status, founders, funding rounds, investors, or subsequent product launch. As a result, this analysis is limited to claims made in the deck itself and cannot verify any later outcomes or financing events.

Sources

Funding and outcome facts on this page were researched on 2026-08-21 from the pages below.

BorrowBot pitch deck slides

BorrowBot pitch deck slide 1 of 12 — BorrowBot pitch deck — slide 1 of 12

BorrowBot pitch deck slide 2 of 12 — BorrowBot pitch deck — slide 2 of 12

BorrowBot pitch deck slide 3 of 12 — BorrowBot pitch deck — slide 3 of 12

BorrowBot pitch deck slide 4 of 12 — BorrowBot pitch deck — slide 4 of 12

BorrowBot pitch deck slide 5 of 12 — BorrowBot pitch deck — slide 5 of 12

BorrowBot pitch deck slide 6 of 12 — BorrowBot pitch deck — slide 6 of 12

What each slide of the BorrowBot pitch deck says

Slide 2

- =P What is BorrowBot? hee ; Borrowbot is an automated system Se which recommends borrowers £2 y the right loan via our Chatbot. (@ J =

Slide 4

[ new ] Borrowbot How can you help me. or | CE Greetings! Thanks for choosing BorrowBot to be your personal loan advisor. ype to reply. 9 &»

Slide 6

LARGE MORTGAGE MARET IN THAILAND Trillian baht 0 2013 2014 2015 2016

Slide 7

STARTING TARGET G BANGKOK RESIDENCE male and female aging from 20-50 SMART PHONE USERS android and ios users, who use fb, line, instragram HAVE A PROFITABLE CAREER either being employed or running a small and meduim business NEED FINANCES mortgages, auto loans, SME loans, Personal loans

Slide text above is read directly from the BorrowBot deck PDF embedded on this page.