TWI756804B - Financial product recommendation system and method - Google Patents
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Abstract
一種金融商品推薦系統，包含伺服器單元及終端電子裝置。伺服器單元，儲存有分別相關於多個參考客戶的多個個人資料，並根據該等個人資料產生多個分別相關於該等參考客戶的個人特徵資料，該等個人特徵資料的每一者包含多個個人特徵值，並根據該等個人特徵資料，使用一預定分群演算法，產生多個群集定義資料。終端電子裝置傳送包含相關於一待分析客戶的一待分析個人資料的一商品建議請求給該伺服器單元。伺服器單元根據待分析個人資料，產生待分析個人特徵資料及該等群集定義資料，產生一相關於該等群集定義資料其中一者的分群結果。A financial product recommendation system includes a server unit and a terminal electronic device. The server unit stores a plurality of personal data respectively related to a plurality of reference customers, and generates a plurality of personal characteristic data respectively related to the reference customers according to the personal data, each of the personal characteristic data includes a plurality of individual characteristic values, and according to the individual characteristic data, a predetermined clustering algorithm is used to generate a plurality of cluster definition data. The terminal electronic device transmits a commodity suggestion request including a personal data to be analyzed related to a customer to be analyzed to the server unit. The server unit generates the personal characteristic data to be analyzed and the cluster definition data according to the personal data to be analyzed, and generates a clustering result related to one of the cluster definition data.
Description
本發明是有關於一種推薦系統，特別是指一種金融商品推薦系統。The present invention relates to a recommendation system, in particular to a financial commodity recommendation system.
據統計，有近六成的民眾不清楚如何理財規劃，包含不清楚退休金制度、不了解自身退休金缺口、不知道從何開始規劃等。因此，便有可能因缺乏退休理財知識而造成錯估儲蓄目標與未來退休後的需求。According to statistics, nearly 60% of the public do not know how to plan financially, including not knowing the pension system, not knowing their own pension gap, and not knowing where to start planning. As a result, lack of financial knowledge for retirement may lead to miscalculation of savings goals and future retirement needs.
目前，若民眾欲進一步了解相關於上述的理財規劃，通常必需要親自到銀行詢問理財專員，而可能出現耗費時間的問題。At present, if the public wants to know more about the above-mentioned financial planning, they usually have to go to the bank to ask the financial management specialist in person, which may lead to time-consuming problems.
因此，本發明的目的，即在提供一種能改善上述先前技術中至少一缺點的金融商品推薦系統。Therefore, the object of the present invention is to provide a financial product recommendation system that can improve at least one of the disadvantages of the above-mentioned prior art.
於是，本發明所提供的金融商品推薦系統包含一伺服器單元，及能經由一通訊網路與該伺服器單元通訊的一終端電子裝置。該伺服器單元儲存有分別相關於多個參考客戶的多個個人資料，及多個分別相關於該等參考客戶的金融商品持有資料。該伺服器單元根據該等個人資料產生多個分別相關於該等參考客戶的個人特徵資料，該等個人特徵資料的每一者包含多個個人特徵值；該伺服器單元根據該等個人特徵資料，使用一預定分群演算法，產生多個群集定義資料，每一群集定義資料包含分別相關於該等個人特徵值的多個特徵值範圍；該終端電子裝置傳送包含相關於一待分析客戶的一待分析個人資料的一商品建議請求給該伺服器單元；該伺服器單元根據該待分析個人資料，產生一待分析個人特徵資料，該待分析個人特徵資料包含多個分別相關於該等群集定義資料的該等特徵值範圍的待分析個人特徵值；該伺服器單元根據該等待分析個人特徵值及該等群集定義資料，產生一相關於該等群集定義資料其中一者的分群結果，其中，該等待分析個人特徵值符合該分群結果所相關的該群集定義資料的該等特徵值範圍；該伺服器單元將對應於該分群結果所相關的該群集定義資料的一金融商品建議組合傳送給該終端電子裝置。Therefore, the financial product recommendation system provided by the present invention includes a server unit, and a terminal electronic device capable of communicating with the server unit via a communication network. The server unit stores a plurality of personal data respectively related to a plurality of reference customers, and a plurality of financial product holding data respectively related to the reference customers. The server unit generates a plurality of personal characteristic data respectively related to the reference clients according to the personal data, each of the personal characteristic data includes a plurality of personal characteristic values; the server unit generates a plurality of personal characteristic data according to the personal characteristic data , using a predetermined grouping algorithm to generate a plurality of cluster definition data, each of which includes a plurality of characteristic value ranges respectively related to the individual characteristic values; the terminal electronic device transmits a data including a A commodity suggestion request of the personal data to be analyzed is requested to the server unit; the server unit generates a personal characteristic data to be analyzed according to the personal data to be analyzed, and the personal characteristic data to be analyzed includes a plurality of definitions respectively related to the clusters The individual characteristic value to be analyzed in the characteristic value range of the data; the server unit generates a clustering result related to one of the cluster definition data according to the pending analysis individual characteristic value and the cluster definition data, wherein, The individual characteristic value to be analyzed conforms to the characteristic value range of the cluster definition data related to the grouping result; the server unit transmits a financial product suggested combination corresponding to the cluster definition data related to the grouping result to the grouping result terminal electronics.
在一些實施態樣中，該伺服器單元還根據該等群集定義資料，利用一非線性迴歸方式，建立分別對應該等群集定義資料的多個收入估算模型，並根據該分群結果將該分群結果所相關的該群集定義資料對應之該收入估算模型設定為一目標收入估算模型；該伺服器單元根據該待分析個人資料並利用該目標收入估算模型，估算出該待分析客戶的一估算個人收入；該伺服器單元根據該估算個人收入產生對應該金融商品建議組合的一金融商品投資金額。In some implementations, the server unit further uses a nonlinear regression method according to the cluster definition data to establish a plurality of income estimation models respectively corresponding to the cluster definition data, and the grouping result is based on the clustering result. The income estimation model corresponding to the relevant cluster definition data is set as a target income estimation model; the server unit estimates an estimated personal income of the customer to be analyzed by using the target income estimation model according to the personal data to be analyzed ; the server unit generates a financial product investment amount corresponding to the financial product suggested combination according to the estimated personal income.
在一些實施態樣中，每一個人資料包含相關於該參考客戶的一地理位置的一地理環境資料，及相關於該參考客戶且公開的一政府資料；該待分析個人資料包含相關於該待分析客戶的一待分析地理位置的一待分析地理環境資料及相關於該待分析客戶且公開的一待分析政府資料。In some implementations, each personal data includes a geographic environment data related to a geographic location of the reference client, and a publicly available government data related to the reference client; the personal data to be analyzed includes information related to the to-be-analyzed client A to-be-analyzed geographic environment data of a customer's geographical location to be analyzed and a to-be-analyzed government data related to the to-be-analyzed customer and made public.
在一些實施態樣中，該地理環境資料相關於地政狀況、公共設施狀況及私人設施狀況其中至少一者；及該政府資料相關於多個年齡的平均餘命、多個行政區的收入與支出，及就業狀況其中至少一者。In some implementations, the geographic environment data is related to at least one of land administration status, public facility status, and private facility status; and the government data is related to average life expectancy of multiple ages, income and expenditure of multiple administrative districts, and at least one of employment status.
在一些實施態樣中，該待分析個人資料所包含的該待分析政府資料是下載自一政府資料庫。In some implementation aspects, the government data to be analyzed included in the personal data to be analyzed is downloaded from a government database.
在一些實施態樣中，該預定分群演算法為k-平均演算法。In some implementation aspects, the predetermined grouping algorithm is a k-means algorithm.
本發明的另一目的在於提供一種金融商品推薦方法，該金融商品推薦方法藉由一金融商品推薦系統執行，該金融商品推薦系統包含一伺服器單元及能經由一通訊網路與該伺服器單元通訊的一終端電子裝置，該伺服器單元儲存有分別相關於多個參考客戶的多個個人資料，及多個分別相關於該等參考客戶的金融商品持有資料；該金融商品推薦方法包含：Another object of the present invention is to provide a financial product recommendation method. The financial product recommendation method is performed by a financial product recommendation system. The financial product recommendation system includes a server unit and can communicate with the server unit via a communication network. a terminal electronic device, the server unit stores a plurality of personal data respectively related to a plurality of reference customers, and a plurality of financial product holding data respectively related to the reference customers; the financial product recommendation method includes:
該伺服器單元根據該等個人資料產生多個分別相關於該等參考客戶的個人特徵資料，該等個人特徵資料的每一者包含多個個人特徵值。The server unit generates a plurality of personal characteristic data respectively related to the reference clients according to the personal data, and each of the personal characteristic data includes a plurality of personal characteristic values.
該伺服器單元根據該等個人特徵資料，使用一預定分群演算法，產生多個群集定義資料，每一群集定義資料包含分別相關於該等個人特徵值的多個特徵值範圍。The server unit uses a predetermined clustering algorithm to generate a plurality of cluster definition data according to the personal characteristic data, and each cluster definition data includes a plurality of characteristic value ranges respectively related to the personal characteristic values.
該終端電子裝置傳送包含相關於一待分析客戶的一待分析個人資料的一商品建議請求給該伺服器單元。The terminal electronic device transmits a commodity suggestion request including a personal data to be analyzed related to a customer to be analyzed to the server unit.
該伺服器單元根據該待分析個人資料，產生一待分析個人特徵資料，該待分析個人特徵資料包含多個分別相關於該等群集定義資料的該等特徵值範圍的待分析個人特徵值。The server unit generates a personal characteristic data to be analyzed according to the personal data to be analyzed, and the personal characteristic data to be analyzed includes a plurality of personal characteristic values to be analyzed which are respectively related to the characteristic value ranges of the cluster definition data.
該伺服器單元根據該等待分析個人特徵值及該等群集定義資料，產生一相關於該等群集定義資料其中一者的分群結果，其中，該等待分析個人特徵值符合該分群結果所相關的該群集定義資料的該等特徵值範圍。The server unit generates a clustering result related to one of the cluster-defining data according to the pending-analyzed personal characteristic value and the cluster-defining data, wherein the pending-analysed personal characteristic value corresponds to the clustering result associated with the clustering result. The clusters define the range of these eigenvalues for the data.
該伺服器單元將對應於該分群結果所相關的該群集定義資料的一金融商品建議組合傳送給該終端電子裝置。The server unit transmits to the terminal electronic device a suggested combination of financial products corresponding to the cluster definition data related to the clustering result.
本發明的功效在於：本發明之金融商品推薦系統藉由該伺服器單元根據該等參考客戶的該等個人特徵資料並利用k-平均演算法產生出該等群集定義資料，以便在接收到來自該終端電子裝置的該商品建議請求時，能根據該等群集定義資料及該商品建議請求所包含的該待分析個人資料產生出該分群結果，進而能夠根據該分群結果將對應之該金融商品建議組合傳送給該終端電子裝置，故確實能達成本發明的目的。The effect of the present invention is that the financial product recommendation system of the present invention generates the cluster definition data by the server unit according to the personal characteristic data of the reference customers and using the k-average algorithm, so as to receive the information from the When the terminal electronic device requests the commodity suggestion, the grouping result can be generated according to the cluster definition data and the to-be-analyzed personal data included in the commodity suggestion request, and then the corresponding financial commodity suggestion can be made according to the clustering result. The combination is transmitted to the terminal electronic device, so the object of the present invention can be achieved indeed.
在本發明被詳細描述之前，應當注意在以下的說明內容中，類似的元件是以相同的編號來表示。Before the present invention is described in detail, it should be noted that in the following description, similar elements are designated by the same reference numerals.
參閱圖1，本發明之金融商品推薦系統的一實施例，包含一伺服器單元10，及能經由一通訊網路200(例如為網際網路)與該伺服器單元10通訊的一終端電子裝置20。Referring to FIG. 1 , an embodiment of the financial product recommendation system of the present invention includes a
該終端電子裝置可以是可攜式電子裝置(例如為智慧型手機、平板電腦、筆記型電腦等)或是個人電腦，但不以上述為限。The terminal electronic device may be a portable electronic device (such as a smart phone, a tablet computer, a notebook computer, etc.) or a personal computer, but is not limited to the above.
於此實施例中，該伺服器單元10例如為歸屬於一銀行的一電腦主機，並適於與一政府資料庫300連線，且儲存有分別相關於多個參考客戶的多個個人資料，及多個分別相關於該等參考客戶的金融商品持有資料。In this embodiment, the
每一個人資料包含所對應的該參考客戶相關的一個人基本資料、相關於該參考客戶的一地理位置的一地理環境資料，及相關於該參考客戶且公開的一政府資料。該個人基本資料可以是該參考客戶的性別、年齡、職業、職稱、收入…等，該地理位置可以是該參考客戶的戶籍地，而該地理環境資料相關於地政狀況、公共設施狀況及私人設施狀況其中至少一者，並例如為距離該地理位置一預設距離內有幾間醫院、該地理位置與最近的捷運站之間的距離、或距離該地理位置一預設距離內有幾個嫌惡設施…等，該政府資料相關於多個行政區的收入與支出，及就業狀況(就業率)，上述僅為舉例說明，並例如為該地理位置位於的行政區的每人平均收入、每人平均支出、該地理位置位於的行政區的就業率…等，並不以上述為限。Each personal data includes a personal basic data related to the corresponding reference customer, a geographic environment data related to a geographic location of the reference customer, and a public government data related to the reference customer. The basic personal data can be the reference customer's gender, age, occupation, job title, income, etc., the geographic location can be the reference customer's domicile, and the geographic environment data is related to land administration, public facilities, and private facilities. At least one of the conditions, such as how many hospitals are within a preset distance from the geographic location, the distance between the geographic location and the nearest MRT station, or the number of hospitals within a preset distance from the geographic location Disgusting facilities... etc. The government information is related to the income and expenditure of multiple administrative districts, and the employment status (employment rate), the above is only an example, and, for example, the average income per person of the administrative district in which the geographical location is located, the average Expenditure, employment rate in the borough in which the geographic location is located, etc., but not limited to the above.
參閱圖1及圖2，以下說明本實施例執行之一分群程序的步驟。Referring to FIG. 1 and FIG. 2 , the steps of a grouping procedure performed by this embodiment are described below.
首先，於步驟S11中，該伺服器單元10根據該等個人資料產生多個分別相關於該等參考客戶的個人特徵資料，該等個人特徵資料的每一者包含多個個人特徵值。First, in step S11, the
更明確地說，每一個人特徵資料所包含的該等個人特徵值例如年齡、年收入、戶籍地與最近的捷運站之間的距離、距離戶籍地一預設距離內的醫院的數量、距離戶籍地一預設距離內的嫌惡設施的數量…等。More specifically, the personal characteristic values included in each personal characteristic data, such as age, annual income, the distance between the place of residence and the nearest MRT station, the number of hospitals within a preset distance from the place of residence, the distance The number of abomination facilities within a preset distance from the place of residence...etc.
接著，於步驟S12中，該伺服器單元10根據該等個人特徵資料，使用一預定分群演算法，產生多個群集定義資料，每一群集定義資料包含分別相關於該等個人特徵值的多個特徵值範圍。Next, in step S12, the
於此實施例中，該預定分群演算法為k-平均演算法(k-means clustering)，而該伺服器單元10所產生的該等群集定義資料每一者所包含的該等特徵值範圍可以是年收入界在150萬~200萬、戶籍地與最近的捷運站之間的距離界在300~500公尺、距離戶籍地一預設距離內的醫院的數量界在2~5間…等，但不以上述為限。更進一步地說，該伺服器單元10在產生出該等群集定義資料後，會再接著建立分別指示出多個金融商品建議組合與該等群集定義資料之間的多個對應關係。而該等金融商品建議組合是在該伺服器單元10產生出該等群集定義資料後，由專家分別針對該等群集定義資料所產生的。In this embodiment, the predetermined clustering algorithm is k-means clustering, and the eigenvalue ranges included in each of the cluster definition data generated by the
參閱圖1及圖3，以下說明本實施例執行之一金融商品推薦程序的步驟。Referring to FIG. 1 and FIG. 3 , the steps of a financial product recommendation program executed by this embodiment are described below.
首先，於步驟S21中，該伺服器單元10接收到來自該終端電子裝置20且相關於一待分析客戶的一待分析個人資料的一商品建議請求。First, in step S21, the
更明確地說，該待分析個人資料包含相關於該待分析客戶的一待分析地理位置的一待分析地理環境資料及相關於該待分析客戶且公開的一待分析政府資料。如同前述，該待分析地理位置可以是該待分析客戶的戶籍地址，而相關於該待分析地理位置的該待分析地理環境資料可以是該待分析客戶的戶籍地址與最近的捷運站之間的距離、距離該待分析客戶的戶籍地址一預定距離內醫院的數量、距離該待分析客戶的戶籍地址一預定距離內嫌惡設施的數量…等。而該待分析政府資料可以是相關於該待分析客戶的戶籍地址的行政區的平均所得，並不以上述為限。於本實施例中，該待分析個人資料所包含的該待分析政府資料是該伺服器單元下載自該政府資料庫300。More specifically, the personal data to be analyzed includes a geographical environment data to be analyzed related to a geographical location to be analyzed of the customer to be analyzed and a government data to be analyzed that is disclosed and related to the customer to be analyzed. As mentioned above, the geographical location to be analyzed may be the household registration address of the customer to be analyzed, and the geographical environment data to be analyzed related to the geographical location to be analyzed may be the distance between the household registration address of the customer to be analyzed and the nearest MRT station distance, the number of hospitals within a predetermined distance from the household registration address of the customer to be analyzed, the number of disgusting facilities within a predetermined distance from the household registration address of the customer to be analyzed, etc. The government data to be analyzed may be the average income of the administrative region related to the household registration address of the customer to be analyzed, which is not limited to the above. In this embodiment, the government data to be analyzed included in the personal data to be analyzed is downloaded from the
接著，在步驟S22中，該終端電子裝置20傳送該商品建議請求給該伺服器單元10後，也就是該伺服器單元10接收到該商品建議請求後，該伺服器單元10根據該商品建議請求的該待分析個人資料，產生一待分析個人特徵資料，該待分析個人特徵資料包含多個分別相關於該等群集定義資料的該等特徵值範圍的待分析個人特徵值。接著執行步驟S23。Next, in step S22, after the terminal
於步驟S23中，該伺服器單元10根據該等待分析個人特徵值及該等群集定義資料，產生一相關於該等群集定義資料其中一者的分群結果。而該伺服器單元10所產生的該分群結果所相關的該群集定義資料的該等特徵值範圍符合該等待分析個人特徵值。In step S23, the
以下列表格舉例說明群集定義資料及該待分析個人資料。
根據以上所示的群集定義資料A及群集定義資料B，由於該待分析個人資料符合該群集定義資料B所包含的該等特徵值範圍，因此，該伺服器單元10所產生的該分群結果便會相關於該群集定義資料B。According to the cluster definition data A and the cluster definition data B shown above, since the personal data to be analyzed conforms to the range of the characteristic values included in the cluster definition data B, the clustering result generated by the
接著，於步驟S24中，該伺服器單元10將對應於該分群結果所相關的該群集定義資料的該金融商品建議組合傳送給該終端電子裝置20。承接前例，對應該群集定義資料A的該金融商品建議組合含有大額保障型保險，而對應該群集定義資料B的該金融商品建議組合含有年金險。Next, in step S24 , the
參閱圖1及圖4，以下說明本實施例執行之一收入估算程序的步驟。Referring to FIG. 1 and FIG. 4 , the steps of an income estimation procedure executed by this embodiment are described below.
在步驟S31中，該伺服器單元10根據該等群集定義資料，利用一非線性迴歸方式，建立分別對應該等群集定義資料的多個收入估算模型。In step S31, the
接著於步驟S32中，該伺服器單元10根據該分群結果將該分群結果所相關的該群集定義資料對應之該收入估算模型設定為一目標收入估算模型。Next, in step S32, the
承接前例，該伺服器單元10便會將該群集定義資料B所對應的該收入估算模型設定為該目標收入估算模型。接著，執行步驟S33。Following the previous example, the
參閱步驟S33，該伺服器單元10設定該目標收入估算模型後，便根據該待分析個人資料並利用該目標收入估算模型，估算出該待分析客戶的一估算個人收入。接著，於步驟S34中，根據該估算個人收入產生對應該金融商品建議組合的一金融商品投資金額。Referring to step S33, after setting the target income estimation model, the
舉例來說，該伺服器單元10會將例如為1/3的該估算個人收入設定為金融商品投資金額，並將該金融商品建議組合及該金融商品投資金額傳送給該終端電子裝置20。For example, the
綜上所述，本發明之金融商品推薦系統藉由該伺服器單元10根據該等參考客戶的該等個人特徵資料並利用k-平均演算法產生出該等群集定義資料，以便在接收到來自該終端電子裝置20的該商品建議請求時，能根據該等群集定義資料及該商品建議請求所包含的該待分析個人資料產生出該分群結果，進而能夠根據該分群結果將對應之該金融商品建議組合傳送給該終端電子裝置20，故確實能達成本發明的目的。To sum up, the financial product recommendation system of the present invention generates the cluster definition data by the
惟以上所述者，僅為本發明的實施例而已，當不能以此限定本發明實施的範圍，凡是依本發明申請專利範圍及專利說明書內容所作的簡單的等效變化與修飾，皆仍屬本發明專利涵蓋的範圍內。However, the above are only examples of the present invention, and should not limit the scope of implementation of the present invention. Any simple equivalent changes and modifications made according to the scope of the patent application of the present invention and the contents of the patent specification are still included in the scope of the present invention. within the scope of the invention patent.
10:伺服器單元 20:終端電子裝置 200:通訊網路 300:政府資料庫 S11~S12:步驟 S21~S24:步驟 S31~S34:步驟 10: Server unit 20: Terminal electronics 200: Communication Network 300: Government Database S11~S12: Steps S21~S24: Steps S31~S34: Steps
本發明的其他的特徵及功效，將於參照圖式的實施方式中清楚地呈現，其中： 圖1是本發明之金融商品推薦系統的一實施例的一硬體連接關係示意圖； 圖2是該實施例執行的一分群程序的一流程圖； 圖3是該實施例執行的一金融商品推薦程序的一流程圖；及 圖4是該實施例執行的一收入估算程序的一流程圖。 Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, wherein: 1 is a schematic diagram of a hardware connection relationship of an embodiment of the financial product recommendation system of the present invention; Fig. 2 is a flow chart of a grouping procedure performed by this embodiment; FIG. 3 is a flow chart of a financial product recommendation program executed by the embodiment; and FIG. 4 is a flow chart of a revenue estimation procedure performed by this embodiment.
10:伺服器單元 20:終端電子裝置 200:通訊網路 300:政府資料庫 10: Server unit 20: Terminal electronics 200: Communication Network 300: Government Database
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