TWM613536U - Investment risk scoring system for fund commodities - Google Patents

Investment risk scoring system for fund commodities Download PDF

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TWM613536U
TWM613536U TW110200912U TW110200912U TWM613536U TW M613536 U TWM613536 U TW M613536U TW 110200912 U TW110200912 U TW 110200912U TW 110200912 U TW110200912 U TW 110200912U TW M613536 U TWM613536 U TW M613536U
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risk
fund
scoring
score
data
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謝信誠
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第一商業銀行股份有限公司
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Abstract

一種基金商品的投資風險評分系統中，資料伺服器蒐集客戶與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為其中至少一者相關聯的歷史金融行為資料以及與個人基本資料、財務情況和投資經驗相關聯的參考風險屬性資料；評分伺服器利用機器學習演算法分析並演算多個由該資料伺服器所蒐集用於模型訓練的樣本資料集，以建立對應於目標參數的評分模型，將該歷史金融行為資料饋入該評分模型以獲得模型評分，且根據該模型評分及對應於該參考風險屬性資料的參考評分，產生該客戶對應於該目標參數的風險評分。In the investment risk scoring system of a fund product, the data server collects historical financial behavior data associated with at least one of customer and fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior, as well as basic personal information. Reference risk attribute data related to data, financial situation and investment experience; the scoring server uses machine learning algorithms to analyze and calculate multiple sample data sets collected by the data server for model training to establish corresponding target parameters The historical financial behavior data is fed into the scoring model to obtain a model score, and based on the model score and the reference score corresponding to the reference risk attribute data, the risk score of the customer corresponding to the target parameter is generated.

Description

對於基金商品的投資風險評分系統Investment risk scoring system for fund commodities

本新型是有關於投資風險評估，特別是指一種對於基金商品的投資風險評分系統。This new model relates to investment risk assessment, especially refers to an investment risk scoring system for fund commodities.

隨著投資資訊***及投資理財商品的推陳出新，投資人的投資行為更趨多元化且更難以捉模，因此，若僅根據投資人如性別、年齡、年收入等現有基本資料來評估投資人，特別是對於基金商品的投資風險等級或偏好，以此方式所獲得的評估結果往往難以充分反映投資人對於基金商品的投資風險等級或偏好。With the explosion of investment information and the introduction of new investment and wealth management products, investors’ investment behaviors have become more diversified and more difficult to grasp. Therefore, if investors are only evaluated based on their existing basic information such as gender, age, annual income, etc., Especially for the investment risk grade or preference of fund products, the evaluation results obtained in this way often cannot fully reflect the investment risk grade or preference of investors for fund products.

因此，為了有效推展如基金商品的金融業務，如何發想出一種能夠有效反映客戶對於基金商品的投資風險評分方式遂成為目前金融機構急需解決的議題之一。Therefore, in order to effectively promote financial services such as fund products, how to develop a scoring method that can effectively reflect the investment risk of customers on fund products has become one of the issues that financial institutions urgently need to address.

因此，本新型的目的，即在提供一種對於基金商品的投資風險評分系統，其能克服現有技術至少一個缺點。Therefore, the purpose of the present invention is to provide an investment risk scoring system for fund commodities, which can overcome at least one shortcoming of the prior art.

於是，本新型所提供的一種對於基金商品的投資風險評分系統用於對一客戶在投資基金商品時的風險評估，並包含一資料伺服器、及一評分伺服器。Therefore, an investment risk scoring system for fund products provided by the present invention is used to evaluate a customer's risk when investing in fund products, and includes a data server and a scoring server.

該資料伺服器操作來蒐集該客戶的歷史金融行為資料與參考風險屬性資料，以及用於模型訓練的多個樣本資料集。該歷史金融行為資料與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為其中至少一者相關聯。該參考風險屬性資料與個人基本資料、財務情況和投資經驗相關聯。每一樣本資料集包含多個分別對應於M個與一目標參數有關的特徵參數的參數值，該等特徵參數與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為相關聯。The data server operates to collect historical financial behavior data and reference risk attribute data of the customer, as well as multiple sample data sets for model training. The historical financial behavior data is associated with at least one of fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior. The reference risk attribute information is related to personal basic information, financial situation and investment experience. Each sample data set contains multiple parameter values corresponding to M characteristic parameters related to a target parameter. These characteristic parameters are related to fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior United.

該評分伺服器連接該資料伺服器以接收該歷史金融行為資料、該參考風險屬性資料和該等樣本資料集，並包括一建模模組、及一評分模組。該建模模組操作來利用機器學習演算法分析並演算該等樣本資料集，以建立一對應於該目標參數的評分模型。該評分模組連接該建模模組且操作來將該歷史金融行為資料饋入該評分模型，以估算出一模型評分，並且至少根據該模型評分、及對應於該參考風險屬性資料的參考評分，產生該客戶對應於該目標參數的風險評分。The scoring server is connected to the data server to receive the historical financial behavior data, the reference risk attribute data and the sample data sets, and includes a modeling module and a scoring module. The modeling module operates to analyze and calculate the sample data sets using machine learning algorithms to establish a scoring model corresponding to the target parameter. The scoring module is connected to the modeling module and operates to feed the historical financial behavior data into the scoring model to estimate a model score, and at least according to the model score and the reference score corresponding to the reference risk attribute data , Generate the customer's risk score corresponding to the target parameter.

本新型對於基金商品的投資風險評分系統中，該評分伺服器還包含一輸出模組。該輸入模組輸連接該評分模組，並操作來輸出對應於該客戶且含有該風險評分的風險評估結果。In the new investment risk scoring system for fund commodities, the scoring server also includes an output module. The input module is connected to the scoring module, and operates to output a risk assessment result corresponding to the customer and containing the risk score.

本新型對於基金商品的投資風險評分系統中，該機器學習演算法包含一隨機森林演算法，該評分模型包含多個決策樹，每一決策樹是依據m個選自該Ｍ個特徵參數的對應特徵參數進行決策演算且m＜Ｍ。In the new investment risk scoring system for fund commodities, the machine learning algorithm includes a random forest algorithm, and the scoring model includes a plurality of decision trees, and each decision tree is based on the corresponding m selected from the M feature parameters The characteristic parameters are used for decision-making calculation and m<M.

本新型對於基金商品的投資風險評分系統中，該目標參數為配置於基金商品所佔的資產比例。In the new investment risk scoring system for fund commodities, the target parameter is the proportion of assets allocated to fund commodities.

本新型對於基金商品的投資風險評分系統中，該評分模組還根據分別指派給該參考評分與該模型評分的一預定第一權重及一預定第二權重來產生該風險評分，以使該風險評分=(該參考評分×該預定第一權重)+(該模型評分×該預定第二權重)。In the new investment risk scoring system for fund commodities, the scoring module also generates the risk score according to a predetermined first weight and a predetermined second weight respectively assigned to the reference score and the model score, so that the risk Score=(the reference score×the predetermined first weight)+(the model score×the predetermined second weight).

本新型的功效在於：根據客戶與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為及/或***消費行為相關聯的歷史金融行為資料，並利用經由機器學習演算法所建立對應於目標參數的評分模型而估算出的模型評分；然後根據模型評分和對應於客戶之參考風險屬性資料的參考評分所產生的風險評分，相對於現有風險評分方式所得的評分，能夠充分且真實地反映客戶對於基金商品所能承受的風險偏好，因而藉此風險評分能在有效地避免投資風險的前提下對客戶提供後續的基金理財及/或投資規劃服務。The function of this new model is to use historical financial behavior data associated with fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and/or credit card consumption behavior, and use machine learning algorithms to establish the corresponding target The model score estimated by the parameter scoring model; then the risk score generated based on the model score and the reference score corresponding to the customer's reference risk attribute data, compared with the existing risk scoring method, can fully and truly reflect the customer For the risk appetite that the fund product can bear, this risk score can provide clients with follow-up fund management and/or investment planning services on the premise of effectively avoiding investment risks.

在本新型被詳細描述之前，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 represented by the same numbers.

參閱圖1，本新型實施例的一種對於基金商品的投資風險評分系統100用於對一客戶在投資基金商品時的風險評估，並包含一資料伺服器1、及一評分伺服器2。Referring to FIG. 1, an investment risk scoring system 100 for fund products according to a new embodiment of the present invention is used to evaluate the risk of a client when investing in fund products, and includes a data server 1 and a scoring server 2.

在本實施例中，該資料伺服器1例如可以一電腦裝置來實施，且可連接外部資料庫(圖未示)，例如金融服務機構的資料庫，並用於資料蒐集，特別是用來蒐集如銀行機構的金融服務機構之(待評分)客戶的相關資料，以及用於建模所需的所有資料。In this embodiment, the data server 1 can be implemented by, for example, a computer device, and can be connected to an external database (not shown), such as a database of a financial service institution, and used for data collection, especially for collection such as The relevant data of the (to be scored) customer of the financial service institution of the banking institution, as well as all the data required for modeling.

在本實施例中，該評分伺服器2亦可以一電腦裝置來實施，其連接該資料伺服器1，且例如可包含一建模模組21、一連接該建模模組21的評分模組22、及一連接該評分模組22的輸出模組23。該建模模組21和該評分模組22其中每一者可以硬體、軟體、韌體或其組合來實施，而該輸出模組23例如可實施成但不限於一顯示器。In this embodiment, the scoring server 2 can also be implemented by a computer device, which is connected to the data server 1, and may include, for example, a modeling module 21 and a scoring module connected to the modeling module 21 22. And an output module 23 connected to the scoring module 22. Each of the modeling module 21 and the scoring module 22 can be implemented by hardware, software, firmware or a combination thereof, and the output module 23 can be implemented as but not limited to a display, for example.

以下，將參閱圖1及圖2來示例地詳細說明該投資風險評分系統100對於例如屬於一銀行機構且想要投資基金商品的一客戶如何執行一投資風險評分程序。值得注意的是，此處連接該資料伺服器1的外部資料庫例如是該銀行機構所提供的一資料庫。較佳地，此資料庫可提供有關於該銀行機構之所有客戶且更豐富及多元化的金融資料。該客戶個資管理程序例如可包含以下步驟S21~S25。Hereinafter, referring to FIG. 1 and FIG. 2, how the investment risk scoring system 100 executes an investment risk scoring procedure for, for example, a customer who belongs to a banking institution and wants to invest in fund products will be described in detail. It is worth noting that the external database connected to the data server 1 here is, for example, a database provided by the banking institution. Preferably, this database can provide richer and more diversified financial information about all customers of the banking institution. The client personal information management program may include the following steps S21 to S25, for example.

首先，在步驟S21中，該資料伺服器1搜尋例如該銀行機構的資料庫，以蒐集該客戶的歷史金融行為資料與參考風險屬性資料，以及用於模型訓練的多個樣本資料集，並且將蒐集到的該歷史金融行為資料、該參考風險屬性資料和該等樣本資料集傳送給該評分伺服器2。值得注意的是，該歷史金融行為資料與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為其中至少一者相關聯，該參考風險屬性資料與個人基本資料、財務情況和投資經驗相關聯，每一樣本資料集包含多個分別對應於M個與一目標參數有關的特徵參數的參數值，該等特徵參數與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為相關聯。First, in step S21, the data server 1 searches, for example, the database of the banking institution to collect historical financial behavior data and reference risk attribute data of the customer, as well as multiple sample data sets for model training, and The collected historical financial behavior data, the reference risk attribute data and the sample data sets are sent to the scoring server 2. It is worth noting that the historical financial behavior data is related to at least one of fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior. The reference risk attribute data is related to personal basic information, financial status, and Related to investment experience, each sample data set contains multiple parameter values corresponding to M characteristic parameters related to a target parameter. These characteristic parameters are related to fund transaction behavior, fund profit and loss records, other asset status, and digital browsing behavior Related to credit card consumption behavior.

更明確地，該歷史金融行為資料可包含該客戶已購買之基金商品及其損益的相關資料(例如基金商品的平均持有天數、偏好的基金商品的類型、最大虧損金額、報酬續之波動程度)、該客戶持有該銀行機構所發行的其他金融商品以及金融帳戶之帳戶餘額的相關資料、該客戶在該銀行機構所提供如基金理財之服務網站的瀏覽足跡(例如，長、短期的瀏覽次數以及每次瀏覽的時間)的相關資料，以及該客戶使用該銀行機構所發行之***的相關資料(例如，刷卡情況以及是否使用循環利率)。該參考風險屬性資料例如是在該客戶首次申購基金商品前由該客戶透過人為填寫或輸入的方式作答的一風險屬性問卷，此風險屬性問卷的內容可包含例如基本資料(如年齡、職業)、財務背景資料(如、收入、資金來源)和投資經驗資料(如曾購買的金融商品)，且根據此風險屬性問卷的內容經由預定的評分方式，可獲得該客戶的一參考評分。在本實施例中，該目標參數例如為「配置於基金商品所佔的資產比例」，並且例如M=35，但不限於此例。於是，每一樣本資料集可包含例如35個參數值，其中含有：16個與基金交易行為相關聯之「近二年曾經持有債券型基金數量」、「近二年曾經持有股票型基金數量」、「近二年曾經持有多重資產型基金數量」、「近二年曾經持有貨幣型基金數量」、「二年曾經持有平衡型基金數量」、「近二年曾經持有組合型基金數量」、「近二年曾經持有ETF型基金數量」、「平均申購金額」、「平均申購金額佔總資產比例」、「平均持有天數」、「申購次數佔所有交易行為的百分比」、「申購次數佔所有交易行為的百分比」、「申購次數/贖回次數」、「最大獲利金額」、「最大獲利金額-最大虧損金額」、「資產管理規模(AUM)排行分級」等特徵參數的參數值；7個與基金損益紀錄相關聯之「歷史交易基金的平均報酬率」、「歷史交易基金的平均報酬率之波動度」、「歷史持有的基金產品數」、「歷史最大虧損報酬率的波動度」、「歷史最大虧損金額」、「歷史最大虧損金額的波動度」、「獲利因子」等特徵參數的參數值；5個與其他資產狀況相關聯之「台幣活存餘額」、「台幣定存餘額」、「支票存款餘額」、「外幣活存餘額」、「外幣定存餘額」等特徵參數的參數值；4個與數位瀏覽行為相關聯之「最近一個月訪問基金理財網次數」、「最近一個月於基金理財網平均停留時間」、「最近五個月訪問基金理財網次數」、「最近五個月於基金理財網平均停留時間」等特徵參數的參數值；及3個與***消費行為相關聯之「信用額度/年收入」、「是否使用循環利率」、「平均信用額度使用率」等特徵參數的參數值。More specifically, the historical financial behavior data may include relevant information about the fund products purchased by the client and their profits and losses (such as the average number of days of holding fund products, the type of fund products preferred, the maximum amount of loss, and the degree of volatility of the return ), the customer holds other financial products issued by the banking institution and relevant information about the account balance of the financial account, and the customer’s browsing footprint on the banking institution’s service websites such as fund management (for example, long-term and short-term browsing The number of times and the time of each visit), and the relevant information of the customer's use of the credit card issued by the banking institution (for example, the credit card status and whether to use the revolving interest rate). The reference risk attribute data is, for example, a risk attribute questionnaire answered by the customer through manual filling or input before the first purchase of fund products by the customer. The content of this risk attribute questionnaire may include, for example, basic information (such as age, occupation), Financial background data (such as income, source of funds) and investment experience data (such as financial products purchased), and based on the content of the risk attribute questionnaire through a predetermined scoring method, a reference score for the customer can be obtained. In this embodiment, the target parameter is, for example, "proportion of assets allocated to fund commodities", and for example, M=35, but it is not limited to this example. Therefore, each sample data set can contain, for example, 35 parameter values, including: 16 related to fund trading behaviors: "the number of bond funds held in the past two years", "the number of stock funds held in the past two years" "Number", "Number of multiple asset funds held in the past two years", "Number of currency funds held in the past two years", "Number of balanced funds held in the past two years", ``Hold portfolios in the past two years "Number of funds", "Number of ETF funds held in the past two years", "Average purchase amount", "Average purchase amount to total assets ratio", "Average holding days", "Number of purchases as a percentage of all transactions ", "Subscription times as a percentage of all transaction activities", "Subscription times/redemption times", "Maximum profit amount", "Maximum profit amount-maximum loss amount", "Asset management scale (AUM) ranking classification" Parameter values of other characteristic parameters; 7 related to the fund’s profit and loss records: "Average rate of return of historical trading funds", "Volatility of average rate of return of historical trading funds", "Number of fund products held in history", " Parameter values of characteristic parameters such as the volatility of the historical maximum loss return, the volatility of the historical maximum loss, the volatility of the historical maximum loss, and the profit factor; 5 related to other asset conditions "Taiwan Dollar Parameter values of characteristic parameters such as “Live Deposit Balance”, “Taiwan Dollar Fixed Deposit Balance”, “Check Deposit Balance”, “Foreign Currency Live Deposit Balance”, and “Foreign Currency Fixed Deposit Balance”; 4 of them are related to the digital browsing behavior. The number of visits to the fund management network in a month", the "average time spent on the fund management network in the last month", "the number of visits to the fund management network in the last five months", and the ``average stay time on the fund management network in the last five months" and other characteristic parameters Parameter value; and 3 parameter values of characteristic parameters such as "Credit Limit/Annual Income", "Whether to Use Revolving Rate", and "Average Credit Limit Utilization Rate" associated with credit card consumption behavior.

當該評分伺服器2接收到來自該資料伺服器2的該歷史金融行為資料、該參考風險屬性資料和該等樣本資料集時，在步驟S22中， 該建模模組21操作來根據該等樣本資料集，利用機器學習演算法建立一對應於該目標參數的評分模型。更具體地，在本實施例中，該機器學習演算法包含一隨機森林演算法，該評分模型包含多個決策樹，每一決策樹是依據m個選自該Ｍ個特徵參數的對應特徵參數進行決策演算且m＜Ｍ。值得注意的是，隨機森林演算法屬於一種「監督式學習」的演算法，相較於深度學習的神經網絡引算法通常具有交高的模型解釋能力，並且在建模前已定義出該目標參數(即，「配置於基金商品所佔的資產比例」)，如此能利用該等樣本資料集在學習過程中不斷修正以發展出相對正確的評分模型。When the scoring server 2 receives the historical financial behavior data, the reference risk attribute data, and the sample data sets from the data server 2, in step S22, the modeling module 21 operates according to the For the sample data set, a scoring model corresponding to the target parameter is established by using a machine learning algorithm. More specifically, in this embodiment, the machine learning algorithm includes a random forest algorithm, and the scoring model includes a plurality of decision trees, and each decision tree is based on m corresponding feature parameters selected from the M feature parameters Perform decision calculus and m<M. It is worth noting that the random forest algorithm is a kind of "supervised learning" algorithm. Compared with the neural network citation algorithm of deep learning, it usually has a higher model interpretation ability, and the target parameter has been defined before modeling. (That is, the "proportion of assets allocated to fund commodities"), so that these sample data sets can be used to continuously modify in the learning process to develop a relatively correct scoring model.

更明確地，參閱圖3，該建模模組21利用裝袋(Bagging)演算法每次以隨機方式從該等樣本資料集中選取預定數量的樣本資料集作為該次的訓練資料集，並一共執行n次，如此可獲n個訓練資料集。對於每一訓練資料集，為了生成不同的隨機向量θi，i=1，…，n，對從該Ｍ個特徵參數隨機選取的m個特徵參數其中每一者進行節點分割，以選擇達到最小的Gini係數的分割方式進行***而最後生成一棵決策樹。在此情況下，每一棵決策樹可用作一分類器，並可回應於輸入資料而產生一分類結果；然後，該建模模組21還利用分類或回歸演算法匯整並演算由n棵決策樹所產生的n個分類結果，以從多個預定的分類等級決定出一目標分類等級，並輸出對應於該目標分類等級的評分。值得注意的是，由於該評分模型結合有袋裝演算法及分類或回歸演算法，因而可提高模型準確率和穩定性，同時可避免過擬合(Overfitting)的發生而保有模型集成(Ensemble)特性。More specifically, referring to FIG. 3, the modeling module 21 uses a bagging algorithm to randomly select a predetermined number of sample data sets from the sample data sets each time as the training data set for this time, and a total of Perform n times, so that n training data sets can be obtained. For each training data set, in order to generate different random vectors θi, i=1,...,n, perform node segmentation on each of the m feature parameters randomly selected from the M feature parameters to select the smallest The division method of the Gini coefficient is split and finally a decision tree is generated. In this case, each decision tree can be used as a classifier, and can generate a classification result in response to the input data; then, the modeling module 21 also uses classification or regression algorithms to aggregate and calculate The n classification results generated by a decision tree are used to determine a target classification level from a plurality of predetermined classification levels, and output a score corresponding to the target classification level. It is worth noting that because the scoring model combines bagging algorithm and classification or regression algorithm, it can improve the accuracy and stability of the model, while avoiding the occurrence of overfitting and maintaining model integration (Ensemble) characteristic.

之後，在步驟S23中，該評分模組22操作來將該歷史金融行為資料作為輸入資料饋入該評分模型，以估算出對應於該客戶的一模型評分。由於該評分模型的目標參數為「配置於基金商品所佔的資產比例」，若比例越高代表該客戶對基金商品的偏好越高同時亦代表持有基金商品的風險越高，則該模型評分會越高，反之則越低。Then, in step S23, the scoring module 22 operates to feed the historical financial behavior data as input data into the scoring model to estimate a model score corresponding to the customer. Since the target parameter of the scoring model is "the proportion of assets allocated to fund products", if the higher the ratio, the higher the customer’s preference for fund products and the higher the risk of holding fund products, the model scores The higher it will be, the lower it will be otherwise.

然後，在步驟S24中，該評分模組22根據該模型評分、對應於該參考風險屬性資料的該參考評分、 一預定第一權重及一預定第二權重，產生該客戶對應於該目標參數的風險評分。更明確地，該一預定第一權重及該預定第二權重式分別指派給該參考評分與該模型評分，以使該風險評分=(該參考評分×該預定第一權重)+(該模型評分×該預定第二權重)。Then, in step S24, the scoring module 22 generates the customer's corresponding target parameter based on the model score, the reference score corresponding to the reference risk attribute data, a predetermined first weight, and a predetermined second weight. Risk score. More specifically, the predetermined first weight and the predetermined second weight are respectively assigned to the reference score and the model score, so that the risk score = (the reference score × the predetermined first weight) + (the model score ×The predetermined second weight).

最後，在步驟S25中，該輸出模組23輸出含有該風險評分的風險評分結果。舉例來說，該輸出模組23可以顯示出該風險評分結果，或者在其他實施例中，該輸出模組23可以是一通訊模組以將該風險評分結果傳送給一特定使用終端(如銀行機構的一電腦終端或該客戶持有之手機)。Finally, in step S25, the output module 23 outputs the risk score result containing the risk score. For example, the output module 23 may display the risk score result, or in other embodiments, the output module 23 may be a communication module to transmit the risk score result to a specific user terminal (such as a bank A computer terminal of the organization or a mobile phone held by the customer).

於是，該銀行機構可依據該客戶的風險評分提供該客戶後續在基金理財或投資規劃時在基金商品配置(如股債比例)上的建議。Therefore, the banking institution can provide the customer with suggestions on fund product allocation (such as the ratio of stocks and debts) during the subsequent fund management or investment planning based on the customer's risk score.

綜上所述，由於該評分模型是利用與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為相關聯的樣本資料集所訓練而成，因而所獲得的模型評分可真實地反映客戶對於基金商品所能承受的風險偏好。於是，將(客觀的)模型評分結合(主觀的)參考評分所獲得的風險評分，相對於現有風險評分方式所得的評分，能在有效地避免投資風險的前提下對客戶提供後續的基金理財及/或投資規劃服務。因此，本新型投資風險評分系統100確實能達成本新型的目的。In summary, since the scoring model is trained using sample data sets related to fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior, the model score obtained can be realistic Reflect the risk appetite that clients can bear for fund commodities. Therefore, the risk score obtained by combining the (objective) model score with the (subjective) reference score, compared to the score obtained by the existing risk scoring method, can provide customers with follow-up fund management and financial management under the premise of effectively avoiding investment risks. /Or investment planning services. Therefore, the new investment risk scoring system 100 can indeed achieve the purpose of a new model.

惟以上所述者，僅為本新型的實施例而已，當不能以此限定本新型實施的範圍，凡是依本新型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，皆仍屬本新型專利涵蓋的範圍內。However, the above are only examples of the present model. When the scope of implementation of the present model cannot be limited by this, all simple equivalent changes and modifications made in accordance with the patent scope of the present model application and the contents of the patent specification still belong to This new patent covers the scope.

100:投資風險評分系統 1:資料伺服器 2:評分伺服器 21:建模模組 22:評分模組 23:輸出模組 S21-S25:步驟100: Investment risk scoring system 1: Data server 2: Scoring server 21: Modeling module 22: Scoring module 23: output module S21-S25: steps

本新型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是一方塊圖，示例地說明本新型實施例對於基金商品的投資風險評分系統的架構； 圖2是一流程圖，示例地說明該實施例對於想要投資基金商品的一客戶如何執行一投資風險評分程序；及 圖3是一示意圖，示例地說明本新型實施例所建立的一評分模型的架構。The other features and functions of the present invention will be clearly presented in the embodiments with reference to the drawings, in which: Fig. 1 is a block diagram illustrating the structure of the investment risk scoring system for fund commodities according to the embodiment of the present invention; 2 is a flowchart illustrating how the embodiment performs an investment risk scoring procedure for a customer who wants to invest in fund products; and Figure 3 is a schematic diagram illustrating the scoring model established by the embodiment of the present invention. Architecture.

100:投資風險評分系統 100: Investment risk scoring system

1:資料伺服器 1: Data server

2:評分伺服器 2: Scoring server

21:建模模組 21: Modeling module

22:評分模組 22: Scoring module

23:輸出模組 23: output module

Claims (5)

一種對於基金商品的投資風險評分系統，用於對一客戶在投資基金商品時的風險評估，並包含：一資料伺服器，操作來蒐集該客戶的歷史金融行為資料與參考風險屬性資料，以及用於模型訓練的多個樣本資料集，該歷史金融行為資料與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為其中至少一者相關聯，該參考風險屬性資料與個人基本資料、財務情況和投資經驗相關聯，每一樣本資料集包含多個分別對應於M個與一目標參數有關的特徵參數的參數值，該M個特徵參數與基金交易行為、基金損益紀錄、其他資產狀況、數位瀏覽行為和***消費行為相關聯；及一評分伺服器，連接該資料伺服器以接收該歷史金融行為資料、該參考風險屬性資料和該等樣本資料集，並包括一建模模組，操作來利用機器學習演算法分析並演算該等樣本資料集，以建立一對應於該目標參數的評分模型，及一評分模組，連接該建模模組且操作來將該歷史金融行為資料饋入該評分模型，以估算出一模型評分，並且至少根據該模型評分、及對應於該參考風險屬性資料的參考評分，產生該客戶對應於該目標參數的風險評分。 An investment risk scoring system for fund products, used to assess the risk of a customer when investing in fund products, and includes: a data server that operates to collect the customer’s historical financial behavior data and reference risk attribute data, and use In the model training multiple sample data sets, the historical financial behavior data is associated with at least one of fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior, and credit card consumption behavior. The reference risk attribute data is related to personal basic Data, financial situation and investment experience are related. Each sample data set contains multiple parameter values corresponding to M characteristic parameters related to a target parameter. The M characteristic parameters are related to fund transaction behavior, fund profit and loss records, and others. Asset status, digital browsing behavior and credit card consumption behavior; and a scoring server connected to the data server to receive the historical financial behavior data, the reference risk attribute data and the sample data sets, and include a modeling model Group, operate to use machine learning algorithms to analyze and calculate the sample data sets to establish a scoring model corresponding to the target parameter, and a scoring module, connect the modeling module and operate to the historical financial behavior The data is fed into the scoring model to estimate a model score, and at least the model score and the reference score corresponding to the reference risk attribute data are used to generate the risk score of the customer corresponding to the target parameter. 如請求項1所述對於基金商品的投資風險評分系統，其中該評分伺服器還包含：一輸出模組，連接該評分模組，並操作來輸出對應於該客戶且含有該風險評分的風險評估結果。 For the investment risk scoring system for fund commodities as described in claim 1, wherein the scoring server further includes: an output module, connected to the scoring module, and operated to output a risk assessment corresponding to the client and containing the risk score result. 如請求項1所述對於基金商品的投資風險評分系統，其中，該機器學習演算法包含一隨機森林演算法，該評分模型包含多個決策樹，每一決策樹是依據m個選自該M個特徵參數的對應特徵參數進行決策演算且m<M。 The investment risk scoring system for fund commodities as described in claim 1, wherein the machine learning algorithm includes a random forest algorithm, and the scoring model includes a plurality of decision trees, and each decision tree is based on m selected from the M The corresponding characteristic parameters of the two characteristic parameters are subjected to decision calculus and m<M. 如請求項3所述對於基金商品的投資風險評分系統，其中，該目標參數為配置於基金商品所佔的資產比例。 For the investment risk scoring system for fund commodities as described in claim 3, the target parameter is the proportion of assets allocated to fund commodities. 如請求項1所述對於基金商品的投資風險評分系統，其中，該評分模組還根據分別指派給該參考評分與該模型評分的一預定第一權重及一預定第二權重來產生該風險評分，以使該風險評分=(該參考評分×該預定第一權重)+(該模型評分×該預定第二權重)。 For the investment risk scoring system for fund products as described in claim 1, wherein the scoring module further generates the risk score according to a predetermined first weight and a predetermined second weight respectively assigned to the reference score and the model score , So that the risk score=(the reference score×the predetermined first weight)+(the model score×the predetermined second weight).
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI776370B (en) * 2021-01-25 2022-09-01 第一商業銀行股份有限公司 Investment risk scoring method and system for fund commodities
TWI800955B (en) * 2021-10-20 2023-05-01 中國信託商業銀行股份有限公司 Investment recommendation method and system

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
TWI776370B (en) * 2021-01-25 2022-09-01 第一商業銀行股份有限公司 Investment risk scoring method and system for fund commodities
TWI800955B (en) * 2021-10-20 2023-05-01 中國信託商業銀行股份有限公司 Investment recommendation method and system

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