Dealer profit based auto loan optimization method and server performing thereof

Abstract

A dealer margin-based financial condition optimization server according to the present invention comprises: a data collection module that collects internal data of a dealer from a dealer server and collects external conditions from an external financial institution server; a condition calculation module that calculates internal conditions using the internal data of the dealer; an external condition comparison module that calculates and calculates optimal financial conditions by comparing the internal conditions of the dealer and the external conditions of the external financial institution; and a visualization module that displays the results of a competitiveness evaluation based on the comparison of the internal conditions and the external conditions.

Description

Technology Field

[0001] The present invention relates to a method for optimizing financial conditions based on dealer margins and a server for executing the same. More specifically, it relates to a method for optimizing financial conditions based on dealer margins and a server for executing the same, which enables a dealer to automatically calculate optimal financial conditions to be presented to consumers based on actual data such as the dealer's internal margin rate, inventory status, vehicle residual value, credit card company fees, and financial institution interest rates, which are not provided by existing external platforms. Background Technology

[0003] The automotive sales market has seen an acceleration of online-based digital transformation in recent years. Traditional offline showroom-centered sales methods are revealing limitations in terms of customer accessibility and efficiency. Furthermore, as the complexity of financing terms (leasing, installment, rental, etc.) increases, consumers are showing a strong tendency to use financing comparison platforms (e.g., Cano, Chago, Charge, etc.) to find more favorable terms.

[0004] These financial condition comparison platforms focus on simply comparing basic financial conditions, such as monthly payments, advance payments, and interest rates, based on data provided by various stakeholders, including credit card companies, financial institutions, and OEMs (automobile manufacturers).

[0005] However, these platforms have a fatal limitation in that they fail to reflect actual price competitiveness factors held by each official dealer, such as their actual operating margins, inventory levels, vehicle residual values, and individual promotions.

[0006] Dealerships possess the capability to provide customized financial conditions to customers by internally combining various variables (e.g., the difference between the ex-factory price and the actual selling price, dealer margins, financing partnership terms, credit card fee policies, residual value strategies by vehicle, etc.).

[0007] However, because external platforms fail to properly reflect dealer conditions or remain at the level of simple comparison, consumers find it difficult to verify the actual favorable terms dealers can offer, and dealers are unable to demonstrate their own sales competitiveness due to the commissions and competition from external platforms.

[0008] Furthermore, dealerships are also facing issues in terms of sales conversion management. For example, while dealerships acquire customer leads through online or offline channels, they lack quantitative analysis and data-driven management regarding whether those customers have reviewed financing terms, booked a test drive, or converted to a contract.

[0009] Consequently, there is currently a lack of an integrated management system capable of analyzing and optimizing the conversion flow in real time, where 50 out of 100 leads confirm conditions, 30 proceed to test drives, and 10 sign contracts. Meanwhile, most existing chatbot-based consultation systems remain limited to providing FAQs, simple Q&A, or accepting reservations.

[0010] Sales personnel at customers or dealerships must individually calculate financial terms or manually compare them with external platforms, and chatbots lack the capabilities to calculate margins and financial conditions or propose optimal, customized financial packages. As a result, dealerships experience reduced sales efficiency and digital responsiveness, and situations frequently arise where they must rely on competing platforms. The problem to be solved

[0012] The present invention aims to provide a dealer margin-based financial condition optimization method and a server for executing the same, which enables a dealer to automatically calculate optimal financial conditions to be presented to consumers based on actual data such as the dealer's internal margin rate, inventory status, vehicle residual value, credit card company fees, and financial institution interest rates, which are not provided by existing external platforms.

[0013] Furthermore, the present invention aims to provide a method for optimizing dealer margin-based financial conditions and a server for executing the same, which compares and analyzes financial conditions provided by an external platform with internal conditions in real time, quantifies the differences, and enables dealers to clearly identify more competitive conditions, thereby designing various pricing policies and financial products based on margin adjustments.

[0014] In addition, the present invention aims to provide a method for optimizing dealer margin-based financial conditions and a server for executing the same, which reduces the burden on dealer consulting personnel and standardizes the quality of customer response by having an AI chatbot calculate financial conditions in real time and respond, and provides time and cost savings effects by having the chatbot automate test drive reservations, quotation provision, contract procedures, etc.

[0015] Furthermore, the present invention aims to provide a method for optimizing dealer margin-based financial conditions and a server for executing the same, which can be distributed to multiple dealers in the same SaaS form, thereby enabling expansion to OEMs (automotive manufacturers) or nationwide dealer networks to develop into a new B2B revenue model.

[0016] The present invention aims to provide a method for optimizing dealer margin-based financial conditions and a server for executing the same, which provides various sales indicators such as conversion rates, test drive application rates, and contract signing rates as data, thereby enabling dealers to make scientific decisions and formulate marketing strategies. means of solving the problem

[0018] A dealer margin-based financial condition optimization server for achieving this purpose includes: a data collection module that collects internal data from a dealer company server and external conditions from an external financial company server; a condition calculation module that calculates internal conditions using the internal data of the dealer company; an external condition comparison module that calculates and calculates optimal financial conditions by comparing the internal conditions of the dealer company and the external conditions of the external financial company; and a visualization module that displays the results of a competitiveness evaluation based on the comparison of the internal conditions and the external conditions.

[0019] In one embodiment, the condition calculation module can calculate internal conditions including monthly payments, total interest costs, and initial payments based on the dealer's vehicle price, margin rate, inventory status, and contract conditions.

[0020] In one embodiment, the external condition comparison module performs a comparison based on the difference in monthly payments between the internal condition and the external condition, and if the difference is greater than or equal to a predefined threshold, it can recalculate the internal condition by adjusting the dealer's margin rate.

[0021] In one embodiment, the visualization module may provide the internal and external conditions to the user interface in the form of a graph, chart, or scored rating.

[0022] In addition, a dealer margin-based financial condition optimization method executed on a dealer margin-based financial condition optimization server to achieve this purpose includes: (1) receiving internal data of a dealer from a dealer server; (2) receiving external conditions from an external financial institution server; (3) calculating internal conditions using the internal data of the dealer; and (4) comparing internal conditions and external conditions, and if the difference amount is greater than or equal to a preset threshold, adjusting the margin rate to recalculate the internal conditions and providing them to the dealer server. Effects of the invention

[0024] According to the present invention as described above, there is an advantage in that it can automatically calculate optimal financial conditions for a dealer to present to a consumer based on actual data such as the dealer's internal margin rate, inventory status, vehicle residual value, credit card fees, and financial institution interest rates, which existing external platforms cannot provide.

[0025] In addition, according to the present invention, there is an advantage in that financial conditions provided by an external platform and internal conditions are compared and analyzed in real time, and the differences are quantified so that dealers can clearly identify more competitive conditions, thereby enabling the design of various pricing policies and financial products based on margin adjustments.

[0026] In addition, according to the present invention, the AI ​​chatbot can reduce the burden on dealership consulting personnel and standardize the quality of customer service by calculating financial conditions in real time and responding, and the chatbot can automate test drive reservations, quotation provision, contract procedures, etc., thereby providing time and cost savings.

[0027] In addition, according to the present invention, since it can be distributed to multiple dealers in the same SaaS form, it has the advantage of being able to be expanded to OEMs (automotive manufacturers) or nationwide dealer networks and developed into a new B2B revenue model.

[0028] In addition, according to the present invention, various sales indicators such as conversion rates, test drive application rates, and contract signing rates are provided as data, which has the advantage of enabling dealers to make scientific decisions and formulate marketing strategies. Brief explanation of the drawing

[0030] FIG. 1 is a network configuration diagram for explaining a dealer margin-based financial condition optimization system according to one embodiment of the present invention. FIG. 2 is a block diagram illustrating the internal structure of a dealer margin-based financial condition optimization server according to an embodiment of the present invention. FIG. 3 is a block diagram illustrating the internal structure of a dealer margin-based financial condition optimization server according to another embodiment of the present invention. FIG. 4 is a flowchart illustrating an embodiment of a dealer margin-based financial condition optimization method according to the present invention. Specific details for implementing the invention

[0031] The aforementioned objectives, signatures, and advantages are described in detail below with reference to the attached drawings, thereby enabling those skilled in the art to easily implement the technical concept of the present invention. In describing the present invention, detailed descriptions of known technologies related to the present invention are omitted if it is determined that such descriptions would unnecessarily obscure the essence of the invention. Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings. In the drawings, the same reference numerals are used to indicate the same or similar components.

[0033] FIG. 1 is a network configuration diagram for explaining a dealer margin-based financial condition optimization system according to one embodiment of the present invention.

[0034] Referring to FIG. 1, the dealer margin-based financial condition optimization system includes a dealer company server (100), a customer terminal (200), a dealer margin-based financial condition optimization server (300), an external financial company server (400), and an AI chatbot providing server (500).

[0035] The dealer server (100) centrally manages vehicle sales conditions, inventory information, margin information, etc. managed by the dealer, and provides this information to the dealer margin-based financial condition optimization server (300) in real-time or periodically, thereby performing the function of delivering key information for calculating internal conditions and comparing with external conditions.

[0036] More specifically, the dealer server (100) stores vehicle-specific metadata such as inventory status by vehicle model, option specifications, supply availability schedule, and brand-specific incentive conditions, and provides this information through API or database integration in response to a request from the dealer margin-based financial condition optimization server (300).

[0037] In this process, a pricing policy is also provided, which includes basic financing condition templates applicable to each vehicle (e.g., installment period, down payment settings, whether an in-house finance company is affiliated, etc.) and margin information (M) based on in-house sales policies.

[0038] Additionally, the dealer server receives user response data collected through the AI ​​chatbot providing server (400) and the customer terminal (200), and analyzes and stores lead conversion-related data to determine which vehicle the customer is interested in, under what conditions the customer showed a response, and whether there is a high probability of conversion.

[0039] During this process, the results of the external condition comparison and internal proposed conditions received are provided to dealer terminals or sales personnel in a visualized form, enabling dealers to assess competitive conditions against external financial platforms in real time and respond strategically.

[0040] In this way, the dealer server (100) is linked with the dealer margin-based financial condition optimization server (300) based on information reflecting the dealer's sales strategy and internal profit structure, and provides key input values ​​that have a substantial impact on the calculation of internal conditions (P_final), while also providing a basis for follow-up actions based on customer response data and external comparison results, thereby playing a key role as the starting point for the overall condition optimization and conversion management process of the present invention.

[0041] The customer terminal (200) is a user interface device used by an end user (consumer) who wishes to purchase a vehicle to view and compare financial conditions, and includes various types of digital terminals such as smartphones, tablets, and PCs.

[0042] The customer terminal (200) operates in conjunction with the AI ​​chatbot providing server (400), collects purchase intent and financial condition preferences based on information entered by the user through natural language or a selection interface, converts them into structured parameters, and then transmits them to the dealer margin-based financial condition optimization server (300).

[0043] First, the customer inputs information such as the vehicle model of interest, purchase method (installment / lease), desired installment period, monthly payment limit, card company held, presence of advance payment, and occupation group through the UI of the customer terminal. This information is collected through a natural language processing-based chatbot interface or a structured input window. The input information is transmitted to the dealer margin-based financial condition optimization server (300) via the AI ​​chatbot server (400), and when the dealer margin-based financial condition optimization server (300) derives a comparison result between internal conditions (P_final) and external conditions (P_ext), the result is provided to the customer terminal (200) in a visualized form.

[0044] Customers can check multiple financial conditions presented through a customer terminal (200) and compare detailed information such as the monthly payment, contract period, interest rate, availability of cashback, and vehicle option application status for each condition. The results of this comparison are displayed as an intuitive UI, such as a table, graph, or rating table, through a visualization module (340).

[0045] If the customer is satisfied with the conditions, they can choose conversion actions such as requesting a consultation, booking a test drive, or expressing an intention to purchase, and such response is transmitted to the dealer server (100) through the conversion management module (350) and leads to subsequent marketing or sales activities.

[0046] The dealer margin-based financial condition optimization server (300) calculates internal financial conditions (P_final) based on internal margin information received from the dealer company server (100), analyzes competitiveness by comparing it with external financial conditions (P_ext) received from the external financial company server (400), and then intuitively provides the comparison result through the dealer company terminal (100). This dealer margin-based financial condition optimization server (300) will be described in detail below.

[0047] The external financial company server (400) is a server of a third financial platform or financial service provider and provides various financial information related to automobile installment and lease products, financial conditions, promotions, interest rates, screening policies, etc. to the dealer margin-based financial condition optimization server (300).

[0048] First, the external financial company server (400) provides financial condition data applicable to a specific vehicle model or contract conditions (installment / lease distinction, contract period, advance payment, security deposit, etc.) to the data collection module (310) of the dealer margin-based financial condition optimization server (300). These conditions include monthly payments (P_ext), annual interest rates, total payment amounts, availability of options, brand-specific promotions, availability of cashback, etc.

[0049] When a condition query including specific customer conditions (e.g., credit rating, contract period, presence or absence of advance payment, etc.) is generated from the dealer margin-based financial condition optimization server (300), the external financial company server (400) returns real-time quote data suitable for the customer.

[0050] The external financial company server (400) includes a unique financial product identifier (ID) for each financial condition provided, and the identifier is linked to a comparison result displayed on a dealer terminal (100) or a customer terminal (200) to support accurate product matching when linking contracts or consultations in the future. At this time, since financial conditions fluctuate frequently depending on market interest rates, new car launches, policy changes, etc., the external financial company server maintains a linked state with the condition optimization server and performs an automatic update function to the latest financial conditions. As a result, the system can always reflect the latest competitive external financial conditions.

[0051] Additionally, the external financial institution server (400) includes information regarding internal screening criteria or policy constraints (e.g., specific age, income group restrictions, corporate exclusion conditions, etc.) in the form of metadata, so that when comparing conditions, the dealer margin-based financial condition optimization server (300) can consider whether there are constraints in addition to simple numerical values.

[0052] In this way, the external financial company server (400) functions as an external reference point for the condition comparison and competitiveness judgment algorithm according to the present invention, and plays a key role in enabling quantitative comparison with internal conditions (P_final) proposed by the dealer company by providing various financial condition data in real time or periodically, and ultimately guiding consumers to better financial conditions.

[0053] The AI ​​chatbot providing server (500) includes an artificial intelligence chatbot that processes the user's vehicle purchase consultation based on a natural language interface, thereby collecting the user's requirements in real time and providing personalized financial conditions by linking with the dealer margin-based financial condition optimization server (300).

[0054] The AI ​​chatbot providing server (500) receives a user query entered in natural language from a customer terminal (200) (e.g., “How much is the 36-month installment plan for the Sonata?”). At this time, the AI ​​chatbot providing server (500) automatically extracts financial parameters such as the vehicle model, the number of installment months, the type of card company, the initial payment, and the monthly payment limit through a Natural Language Processing (NLP) module.

[0055] The AI ​​chatbot providing server (500) converts the extracted user request conditions into a structured query form and transmits it to the dealer margin-based financial condition optimization server (300). At this time, for values ​​not specified by the user, a request is made to apply default options (default interest rate, standard contract period, etc.) or to receive recommended conditions from the server.

[0056] The AI ​​chatbot providing server (500) receives results from the dealer margin-based financial condition optimization server (300) that include data such as internal conditions (Pfinal), external conditions (Pext), grade evaluation results, monthly payment comparison results, whether margins are adjusted, and precautions. The AI ​​chatbot providing server (500) generates a natural language description based on the data and can respond, for example, as follows. The AI ​​chatbot providing server (500) can respond, “Based on a 36-month installment plan for a Sonata vehicle, the monthly payment is 420,000 won, which is 20,000 won cheaper than external platforms. The current condition is recommended as the highest grade (Grade A).”

[0057] The AI ​​chatbot providing server (500) performs a request for a re-quest by modifying parameters based on the history of previous queries when a user asks additional questions such as “What if I look at another card company?” or “What if I switch to 60 months?”. For such repetitive queries, it also provides updated condition comparison results while maintaining the conversation context. Additionally, the AI ​​chatbot providing server (500) is configured to convert the condition comparison results into visualization formats such as tables, graphs, and icons, and render them on the dealer terminal or customer terminal UI.

[0058] When a positive response from a user (e.g., “I’ll apply”, “Connect me to a consultation”) is detected by the AI ​​chatbot providing server (500), the corresponding data is transmitted to the conversion management module (350) to facilitate the consultation reservation, test drive application, and contract conversion process. At the same time, the corresponding question and answer history is stored in the database (360) and utilized in the future to improve the AI ​​chatbot’s response and enhance condition recommendations.

[0060] FIG. 2 is a block diagram illustrating the internal structure of a dealer margin-based financial condition optimization server according to an embodiment of the present invention.

[0061] Referring to FIG. 2, the dealer margin-based financial condition optimization server (300) includes a data collection module (310), a condition calculation module (320), an external condition comparison module (330), a visualization module (340), a conversion management module (350), and a database (360).

[0062] The data collection module (310) performs the role of collecting various data required for calculating vehicle financing conditions in real time from the dealer server (100) and the external financial platform, standardizing them, and providing them to the condition calculation module (320) and the external condition comparison module (330).

[0063] The data collection module (310) first performs data linkage with the dealer server (100). To this end, it requests and receives information such as vehicle-specific inventory information, internal margin rate (M), vehicle residual value (Rv), credit card company fee (Fc), and financial company interest rate (If) from the dealer server (100) through a predefined API or data interface. At this time, the collected data undergoes normalization and standardization processes so that it can be directly utilized by the condition calculation module (320).

[0064] Next, the data collection module (310) performs a connection with an external financial platform. The external financial platform includes various services that provide comparison of consumer financial conditions, such as Cano, Charge, and Garage, and the data collection module (310) acquires data such as interest rates, monthly payments, advance payments, and contract options provided by the external platform in real time through API connection, data feed reception, or web crawling.

[0065] The acquired external condition data is converted into an integrated format through mapping and data parsing processes within the module to resolve differences in format between platforms, and is then transmitted to the external condition comparison module (330).

[0066] In addition, a data collection module (310) according to one embodiment of the present invention includes validation logic to ensure the reliability of the collected data. For example, if a financial company interest rate exceeds a predefined allowable range or if a missing value occurs, the module corrects or discards the data and performs a re-request process.

[0067] The collected data stores frequently referenced key condition values ​​in a cache to minimize response speed when calculating conditions, and when a condition change event occurs from a financial institution or an external platform, the database is immediately updated via an event-based trigger, and this updated information is reflected in real time in the condition calculation module (320) and the external condition comparison module (330).

[0068] In addition, this module records data collection events and historical information as logs to manage data collection history by dealer and financial institution, and stores collection failure history for audit purposes. This log data can be utilized for analyzing dealer sales strategies, verifying system performance, tracking errors, and improving data quality.

[0069] Accordingly, the data collection module (310) according to the present invention quickly and accurately collects and normalizes internal data of the dealer company and external financial conditions, thereby ensuring the computational accuracy of the condition calculation module (320) and providing a foundation to present competitive financial conditions to customers based on the latest financial conditions.

[0070] The condition calculation module (320) calculates and calculates optimal financial conditions that can be utilized by consumers or dealers based on internal dealer data (inventory information, margin rate, residual value, card company fees, financial company interest rates, etc.) received from the data collection module (310) and conditions of an external financial platform.

[0071] First, the condition calculation module (320) initializes input data including the vehicle ex-factory price (V), option price, contract period (T), residual value (Rv), financial company interest rate (If), credit card company fee (Fc), and dealer margin rate (M). In this process, the dealer inventory and margin data provided by the data collection module (310) is converted into a standardized form suitable for contract options (lease, installment, rental, etc.) and used in the calculation of the condition calculation module (320).

[0072] At this time, the condition calculation module (320) calculates the basic monthly payment (P_base) based on the net amount excluding the advance payment and residual value from the vehicle price. This calculation is performed by applying the financial company interest rate (If) to calculate the amount to be paid equally over the contract period, and is defined as [Equation 1] below.

[0074] [Mathematical Formula 1]

[0075]

[0077] P base : Monthly payment,

[0078] V: Vehicle ex-factory price (Vehicle base price + Option price)

[0079] M: Dealer margin,

[0080] R v : Vehicle residual value (estimated value at the end of the contract)

[0081] I f : Financial company interest rates,

[0082] T: Contract period,

[0084] The above [Mathematical Formula 1] is intended to calculate the base value of the amount the customer must pay each month by comprehensively considering the vehicle price, residual value, financial company interest rate, and contract period. First, the total price of the vehicle (V) serves as the basis for the total amount that the contract holder must repay to use the vehicle. However, in financial products such as leases or installments, the residual value (Rv) of the vehicle is guaranteed at the end of the contract, so the principal amount to be actually repaid is limited to V–Rv. This corresponds to the depreciation cost (decrease in vehicle value) of the vehicle during the contract period.

[0085] Next, the financial institution interest rate (If) refers to the interest rate payable to the financial institution as consideration for the use of the vehicle. In this invention, it is assumed that this interest rate is applied cumulatively over the contract period, and as a result, the total repayment amount is (V-Rv)*(1+If) T It is expressed as. Here, (1+If) TIt represents the effect of the financial institution's interest rate being reflected in a compounding manner over the contract period T.

[0086] Finally, the total repayment amount calculated in this way is divided equally by the contract period T (in months) to become the basic monthly payment (Pbase) that the contract holder must pay each month.

[0087] The above [Mathematical Formula 1] reflects all four key elements—vehicle price, residual value, financial institution interest rate, and contract period—to provide a standard value for a reasonable monthly payment that the contract holder must actually bear. Through this, dealerships can calculate more accurate and competitive financial conditions than external financial platforms.

[0088] Subsequently, the dealer margin rate (M) and the credit card company fee (Fc) are reflected in the calculated base monthly payment (Pbase) to calculate the dealer's internal condition result (Pfinal) to be presented to the customer. This is performed according to the following [Equation 2].

[0090] [Mathematical Formula 2]

[0091]

[0093] P final : Result of calculation of internal dealer conditions

[0094] P base : Monthly payment,

[0095] M: Dealer margin,

[0096] V: Vehicle ex-factory price (vehicle base price + option price),

[0097] F c : Credit card company commission rate,

[0099] While dealerships secure a certain percentage of margin when selling vehicles, they need to adjust some of the margin to offer favorable customer conditions in order to strengthen competitiveness against external financial platforms. Accordingly, in the present invention, the dealer margin rate (M) is applied to the basic monthly payment (Pbase) to calculate the monthly payment after margin adjustment as Pbase ≠ (1-M).

[0100] The external condition comparison module (330) compares internal monthly payment conditions calculated based on the dealer's internal data (vehicle price, residual value, financial company interest rate, credit card company fee, margin rate) with external monthly payment conditions (Pext) provided by external financial platforms (Cano, Charge, Garage, etc.) to quantify the competitiveness of the internal conditions and classify them into grades for management. This grading process enables the dealer to intuitively judge the quality of financial conditions to be presented to customers and is also utilized for condition optimization and the establishment of marketing strategies.

[0101] The external condition comparison module (330) calculates the difference in monthly payments by comparing the internal condition calculation result and the external condition calculation result of the dealership. If the difference is less than 0, it means that the internal condition is more favorable than the external condition, and if the difference is greater than or equal to 0, it means that the internal condition is less favorable than or equal to the external condition.

[0102] In addition, when comparing with external conditions, a comprehensive score is calculated by considering multiple evaluation items such as the total payment amount, whether residual value is guaranteed, contract flexibility, cashback benefits, and vehicle option packages, in addition to the simple difference in monthly payments. This comprehensive score can be defined as a weighted evaluation formula as shown in [Equation 3].

[0103] First, the difference between the monthly payment under internal conditions and the monthly payment under external conditions is defined as ΔPmonthly, and the difference in total payments occurring over the entire contract period is calculated as ΔPtotal. In addition, contract flexibility (eligibility for early termination, possibility of changing options, residual value guarantee conditions, etc.) is quantified and reflected as Δflexibility.

[0104] In particular, since cashback benefits provided by financial institutions or external platforms offer a significant tangible cost-saving effect for users, the difference in cashback amounts is defined as Δcashback and reflected in the score calculation formula. Additionally, differences in vehicle option packages (insurance, maintenance, upgrade options, etc.) or promotional benefits are defined as Δoption and included in the comparative evaluation. Each evaluation item calculated in this way is assigned a pre-set weight of w1, w2, w3, w4, and w5 to calculate a total score. This total score serves as a comprehensive indicator that objectively shows the competitiveness of internal conditions compared to external conditions.

[0106] [Mathematical Formula 3]

[0107]

[0109] S core : Overall score,

[0110] w1, w2, w3, w4, w5: weights representing the importance of each item,

[0111] △P monthly : The difference between the monthly payment under internal conditions (△Pfinal) and the monthly payment under external conditions (△Pext) due to the difference in monthly payments,

[0112] △P total : Difference in total payment amount,

[0113] △P flexibility : A value that scores conditions such as the possibility of early termination, the possibility of changing options, and extension benefits based on differences in contract flexibility,

[0114] △P cashback: The presence or absence of cashback benefits and differences in amounts provided by financial institutions or external platforms,

[0115] △P option : Difference in superior conditions in vehicle option packages (including insurance, maintenance, upgrade options, etc.),

[0117] The calculated total score is classified into different grades according to a predefined grade table. For example, Grade A refers to cases where the monthly payment is 5% or more lower than the external condition or the residual value guarantee is superior, while Grade B refers to cases where it is similar to or slightly lower than the external condition.

[0118] In this invention, the comparison of amounts is not limited to simple amounts, but includes not only the monthly payment (ΔPmonth) but also the total payment amount (ΔP total ) and contract flexibility (ΔP flexibility The overall competitiveness score is calculated by weighting multiple items such as ) and summing them.

[0119] The calculated overall competitiveness score is classified into grades such as A, B, and C according to a pre-set grade table. For example, if the internal conditions are at least 5% lower in monthly payments than the external conditions and the residual value guarantee conditions are superior, it is rated Grade A; if they are similar to or slightly superior to the external conditions, it is rated Grade B; and if they are less favorable than the external conditions, it is rated Grade C.

[0120] If the evaluation result of the external condition comparison module (330) is determined to be grade C or lower, the system automatically executes a margin adjustment algorithm. The margin adjustment algorithm changes the dealer margin rate (M) within an adjustable range [Mmin, Mmax] and recalculates the new monthly payment condition (Pfinal(M')). This optimization process is performed under constraints such as [Equation 4].

[0122] [Mathematical Formula 4]

[0123]

[0125] : Margin rate to minimize the final monthly payment,

[0126] Constraint that the margin rate must not exceed the range of minimum and maximum margin rates set by the dealer,

[0128] Through this, the external condition comparison module (330) finely adjusts the margin rate until it can derive a condition more favorable than the external financial condition.

[0129] Once optimization is complete, the external condition comparison module (330) records the finally derived grade and score results in the database (360) and provides the corresponding conditions to the dealer server (100) or customer server (200) through the visualization module (340). This allows the dealer to present competitive financial conditions to customers in real time, and the condition grading results are used as reference material for future marketing strategies and financial product design.

[0130] Additionally, the external condition comparison module (330) generates financial scenarios based on various contract periods (T) and prepayment ratios (A), and analyzes and compares the monthly payment amount, total payment cost, and residual value guarantee option for each scenario. Through this, the dealer can propose the most favorable combination of financial conditions to the consumer. The scenario analysis can be expressed as a generalized function as shown in [Equation 5].

[0132] [Mathematical Formula 5]

[0133]

[0135] P(T, A): Monthly payment based on contract period T and advance payment ratio A,

[0136] V: Total ex-factory price of the vehicle,

[0137] A·V: Amount corresponding to the down payment of the vehicle price

[0138] Rv: Residual value at contract termination,

[0139] (VA ·V-Rv): Total net amount to be repaid,

[0140] (1+I f ) T : A value reflecting the accumulated financial institution interest rate over the contract period,

[0142] Finally, the external condition comparison module (330) transmits the output result to the external condition comparison module (330) and the visualization module (340) so that the comparison result can be intuitively provided through the interface of the dealer or customer server (200).

[0143] As described above, the external condition comparison module (330) compares the final monthly payment condition derived from the internal condition calculation module (320) with the external conditions collected from an external financial platform to calculate the competitiveness evaluation result as a multidimensional analysis value, and then provides it to the visualization module (340) to support its display in a user-friendly form.

[0144] The visualization module (340) first receives structured analysis data (e.g., monthly payment difference ΔP, total payment difference, contract condition flexibility evaluation score, A / B / C grade information) transmitted from the external condition comparison module (330). Then, it processes the analysis data and converts it into various visual elements such as graphs, tables, bar charts, and gauge charts.

[0145] For example, if the internal condition is 15,000 won cheaper per month than the external condition, the visualization module can display a comparison of the internal and external conditions with a bar graph along with the message “15,000 won per month savings possible compared to the external condition.”

[0146] Additionally, the visualization module (340) provides customized visualization functions based on the user type. For general customers, a graph briefly summarizing only key difference values ​​(ΔP, grade, etc.) can be provided, while dealer managers can view an analysis report in the form of a dashboard that displays detailed items (difference in total payment amount, comparison of residual value, score by contract option) in detail.

[0147] The visualization module (340) transmits the generated visualization data to the dealer server (100) or customer server (200) to enable output as a web-based UI, a mobile application screen, or an analysis report in PDF format. Additionally, when a real-time comparison request occurs, the visualization module immediately updates the comparison results to reflect the latest status.

[0148] If necessary, the visualization module (340) provides comparison data (history chart) with previous analysis results so that customers can easily identify changes in conditions or discount effects over time.

[0149] The conversion management module (350) performs the function of maximizing the efficiency of customer conversion by systematically tracking the entire customer purchase journey (inquiry → test drive → consultation → contract) based on the analysis results provided by the external condition comparison module (330) and the visualization module (340), and analyzing the conversion rate at each stage.

[0150] The conversion management module (350) defines the customer's behavior flow into multiple conversion stages and collects and analyzes data for each stage in real time. For example, the system tracks the number of customers who simply inquired about financial conditions, the number of customers who booked a test drive, the number of customers who proceeded with a consultation, and the number of customers who signed a final contract among all customers, and calculates the conversion rate for each stage. This conversion rate can be calculated using [Equation 6] as shown below.

[0152] [Mathematical Formula 6]

[0153]

[0155] CR i : Conversion rate,

[0156] N i : Number of customers at conversion stage i,

[0157] N i-1 : Number of customers who reached the previous stage,

[0159] The conversion management module (350) identifies the dropout point at each conversion stage based on the calculated conversion rate data, and if the conversion rate falls below a preset threshold at a specific stage, it sends feedback to the condition calculation module (320) or the external condition comparison module (330) to request automatic adjustment of factors such as financial conditions, promotional benefits, and option configurations.

[0160] For example, if the conversion rate is low during the test drive phase, the conversion management module responds by automatically enhancing benefits (providing coupons, free upgrades to specific options) when booking a test drive.

[0161] Additionally, the conversion management module (350) includes an AI-based customer behavior analysis engine to analyze the customer's behavior history (view time, click patterns, whether a consultation request was made, speed of contract progress, etc.) and recommends a combination of financial conditions and options that can expect the highest conversion rate according to the customer type (corporate customer, individual customer, customer who prefers leasing, etc.). For example, if it is analyzed that a customer group of a specific age group is sensitive to cashback benefits, a high cashback weight w4 is reflected when calculating the overall score for that customer group.

[0162] The conversion management module (350) stores analyzed conversion rate results and improvement data in a database (360) to support dealer managers in monitoring long-term conversion rate trends and optimizing marketing strategies. Additionally, if a change in the conversion rate or an increase in customer churn rate at a specific stage is detected in real time, an alert is sent to the dealer server (100) to enable a rapid response.

[0163] The database (360) is a core data storage device that integrates and stores data generated or collected from each module, such as the condition calculation module (320), external condition comparison module (330), visualization module (340), and conversion management module (350). The database (360) hierarchically manages source data, processed data, conversion analysis results, etc., for internal operations and external analysis, and performs the following operation process.

[0164] The database (360) receives data in real time from the data collection module (310) and each operation module (320, 330, 350). The data includes internal dealer data, external financial conditions, analysis result data, customer behavior data, conversion management data, etc.

[0165] The dealer's internal data includes the vehicle price (V), inventory information, residual value (Rv), financial company interest rate (If), credit card fee (Fc), and dealer margin rate (M), and the external financial conditions include the monthly payment (P_ext), total payment amount, advance payment (Aext), cashback information, contract period (Text), financial options, etc. provided by an external platform, and the analysis result data is the final monthly payment (P_ext) of the condition calculation module (320). finalIt includes the difference from external conditions (ΔP), Score value, and A / B / C grade results, customer behavior data includes view logs, test drive reservation status, consultation request history, and contract signing status, and conversion management data may include the conversion rate at each stage (CRi) and conversion rate improvement feedback logs.

[0166] The database (360) preprocesses the collected source data into a form that is easy to compare and analyze. For example, the annual interest rate (APR) provided by an external financial institution is converted into a monthly interest rate, and the basic monthly payment (P_base) can be precalculated and indexed using the total payment amount and residual value information. In addition, customer behavior data is grouped by customer ID and structured into a form suitable for conversion rate analysis.

[0167] The database (360) immediately provides the data required by each module via API or query requests. The condition calculation module (320) refers to an internal financial data set such as vehicle price, interest rate, and margin rate, and the external condition comparison module (330) compares the latest financial conditions from an external platform with the internal P final To compare, the necessary ΔP, Score, and past comparison history are retrieved, and the visualization module (340) performs graph, chart, and history analysis using comparison results and conversion rate data stored in the database (360), and the conversion management module (350) performs conversion funnel analysis by referring to customer behavior logs for each stage.

[0168] The database (360) contains the conversion rate (CR) for each stage calculated by the conversion management module (350). i It stores ) and historical data and enables long-term trend analysis. Through this, changes in conversion rates during a specific period (e.g., monthly, quarterly) can be analyzed, and periods of declining conversion rates can be identified to provide feedback for improvement requests to the condition calculation module (320) or the external condition comparison module (330).

[0170] FIG. 3 is a block diagram illustrating the internal structure of a dealer margin-based financial condition optimization server according to another embodiment of the present invention.

[0171] Referring to FIG. 3, the dealer margin-based financial condition optimization server (300) may further include an AI-based weight correction module (370).

[0172] The AI-based weight correction module (370) performs a policy-based weight correction step and an AI / machine learning-based dynamic adjustment step to increase the accuracy and market adaptability of the comprehensive score calculated by the external condition comparison module (330).

[0173] The AI-based weight correction module (370) comprehensively reflects changes in external financial platforms, dealer sales strategies, and customer behavior data to determine the difference in monthly payments (ΔP_monthly) and the difference in total payments (ΔP total ), contract flexibility (ΔP flexibility ), Cashback (ΔP cashback ), vehicle options (ΔP option Optimize the weights w1~w5 for items such as ).

[0174] First, the AI-based weight correction module (370) initially adjusts the item-specific weights based on the dealer's marketing strategy or predefined policy rules. For example, during a specific promotion period, customers may prioritize vehicle option benefits, so the weight w5 of the option item is adjusted upward to have a greater impact in [Equation 3].

[0175] In one embodiment, the AI-based weight correction module (370) refers to policy rules set by an administrator and automatically adjusts the weight when a specific condition is met. For example, if the cashback amount from an external platform is 300,000 won or more, w4 can be increased by +0.05.

[0176] In another embodiment, the AI-based weight correction module (370) readjusts the relevant weights in real time when an event occurs, such as a change in financial company interest rates, a change in external platform conditions, or a new promotion.

[0177] After the policy correction, the AI-based weight correction module (370) further optimizes the weights by analyzing customer behavior data and market data.

[0178] First, the AI-based weight correction module (370) collects and analyzes the customer's financial condition selection pattern, click data, option change history, contract conversion rate, etc., in real time.

[0179] The AI-based weight correction module (370) inputs the collected data into a machine learning model (e.g., regression analysis, Gradient Boosting, neural network) to learn the relative influence of each item on customer decision-making. For example, if cashback benefits have the greatest influence on contract conversion in recent customer data, the system automatically increases the w4 value.

[0180] As described above, based on the prediction results of the machine learning model, the values ​​of w1 to w5 are corrected in real time and normalized so that the sum of all weights becomes 1.

[0181] The AI-based weight correction module (370) calculates the final weight by sequentially applying the policy correction result and the AI-based dynamic adjustment result. For example, after the policy increases w1 from 0.40 to 0.45, the AI ​​can fine-tune w1 to 0.47 by reflecting the results of customer data analysis. This final weight is directly applied to the Score calculation formula.

[0183] FIG. 4 is a flowchart illustrating an embodiment of a dealer margin-based financial condition optimization method according to the present invention.

[0184] Referring to FIG. 4, the dealer margin-based financial condition optimization server (300) collects internal dealer data for calculating internal conditions (step S310). At this time, the internal dealer data may include inventory information, margin rate, residual value, credit card company fees, financial company interest rates, etc.

[0185] The dealer margin-based financial condition optimization server (300) collects external financial conditions from an external financial server (step S320). The external financial conditions are monthly payments (P ext ), total payment amount, advance payment (A ext ), Cashback information, Contract period (T ext It may include financial options, etc. These data are used as reference values ​​for calculating the actual transaction feasibility and financial conditions of the vehicle.

[0186] The dealer margin-based financial condition optimization server (300) calculates the dealer's internal conditions based on the dealer's internal data (step S330). In this process, the monthly payment, total payment amount, and whether cashback can be provided are calculated by comprehensively considering the advance payment, margin rate, credit card company fee rate, financial company interest rate, etc.

[0187] The dealer margin-based financial condition optimization server (300) determines a grade by comparing internal conditions and external conditions (step S340). The dealer margin-based financial condition optimization server (300) compares the calculated internal conditions and external financial conditions, calculates an evaluation score based on the difference between each item, and can be expressed as an evaluation of A to D, etc., according to a predefined grade range based on the score.

[0188] The dealer margin-based financial condition optimization server (300) recalculates internal conditions by adjusting the margin according to the grade (step S350). If the dealer margin-based financial condition optimization server (300) determines that the evaluation result of the internal conditions is less competitive than the external conditions (e.g., △P monthly≥ threshold θ), the server (300) recalculates the internal conditions by adjusting the margin rate (M) within a certain range within the dealer's profit limit.

[0189] The dealer margin-based financial condition optimization server (300) compares the corrected internal conditions and external conditions again to score and visualize them, and provides the results (step S360).

[0191] Although the present invention has been described by the embodiments and drawings described above, the present invention is not limited to the above embodiments, and various modifications and variations are possible from this description by those skilled in the art to which the present invention pertains. Accordingly, the concept of the present invention should be understood only by the claims set forth below, and all equivalent or analogous variations thereof shall be considered to fall within the scope of the concept of the present invention. Explanation of the symbols

[0193] 100: Dealer server, 200: Customer terminal, 300: Dealer Margin-Based Financial Conditions Optimization Server, 310: Data collection module, 320: Condition Calculation Module, 330: External condition comparison module, 340: Visualization Module, 350: Transition Management Module, 360: Database, 370: AI-based weight correction module 400: External financial institution server, 500: AI chatbot provider server

Claims

Claim 1 A dealer margin-based financial condition optimization server comprises: a data collection module that collects internal data from a dealer company server and collects external conditions from an external financial company server; a condition calculation module that calculates internal conditions using the internal data of the dealer company; an external condition comparison module that calculates and calculates optimal financial conditions by comparing the internal conditions of the dealer company and the external conditions of the external financial company; and a visualization module that displays the results of a competitiveness evaluation based on the comparison of the internal conditions and the external conditions, wherein the external condition comparison module performs a comparison based on the difference in monthly payments (ΔP) between the internal conditions and the external conditions, and if the difference is greater than or equal to a predefined threshold, adjusts the dealer company's margin rate to recalculate the internal conditions. Claim 2 A dealer margin-based financial condition optimization server according to claim 1, characterized in that the condition calculation module calculates internal conditions including monthly payments, total interest costs, and initial payments based on the dealer's vehicle price, margin rate, inventory status, and contract conditions. Claim 3 delete Claim 4 A dealer margin-based financial condition optimization server according to claim 1, wherein the visualization module provides the internal and external conditions in the form of a graph, chart, or scored grade to a user interface. Claim 5 A method for optimizing financial conditions based on dealer margins, executed on a dealer margin-based financial condition optimization server, comprising: (1) receiving internal data of a dealer from a dealer server; (2) receiving external conditions from an external financial institution server; (3) calculating internal conditions using the internal data of the dealer; (4) comparing internal conditions and external conditions, and if the difference amount is greater than or equal to a preset threshold, adjusting the margin rate to recalculate the internal conditions and providing them to the dealer server; wherein step (4) performs a comparison based on the difference in monthly payments (ΔP) between the internal conditions and external conditions, and if the difference is greater than or equal to a preset threshold, adjusts the margin rate of the dealer to recalculate the internal conditions.

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