TW202230232A - Investment risk scoring method and system for fund commodities capable of fully and reliably reflecting the customer's risk preference for fund commodities - Google Patents
Investment risk scoring method and system for fund commodities capable of fully and reliably reflecting the customer's risk preference for fund commodities Download PDFInfo
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本發明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方法及系統。The present invention relates to investment risk assessment, in particular to an investment risk scoring method and system for fund commodities.
隨著投資資訊爆炸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, the investment behavior of investors has become more diversified and more elusive. Therefore, if investors are only evaluated based on existing basic information such as gender, age, annual income, etc. Especially for the investment risk level or preference of fund commodities, the evaluation results obtained in this way are often difficult to fully reflect the investor's investment risk level or preference for fund commodities.
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 commodities, how to develop a risk scoring method that can effectively reflect customers' investment in fund commodities has become one of the urgent issues that financial institutions need to solve.
因此,本發明的目的,即在提供一種對於基金商品的投資風險評分方法及系統,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 method and system for fund commodities, which can overcome at least one disadvantage of the prior art.
於是,本發明所提供的一種對於基金商品的投資風險評分方法用於對一客戶在投資基金商品時的風險評估並利用一電腦系統來執行,並包含以下步驟:(A)蒐集該客戶的歷史金融行為資料與參考風險屬性資料,以及多個用於模型訓練的樣本資料集,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、數位瀏覽行為和信用卡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個與該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、數位瀏覽行為和信用卡消費行為相關聯;(B) 利用機器學習演算法分析並演算該等樣本資料集,以建立一對應於一目標參數的評分模型;(C)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;及(D)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。Therefore, an investment risk scoring method for fund commodities provided by the present invention is used to evaluate a client's risk when investing in fund commodities and use a computer system to execute, and includes the following steps: (A) Collecting the history of the client Financial behavior data and reference risk attribute data, as well as multiple sample data sets for model training, the historical financial behavior data and at least one of fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior and credit card consumption behavior Associated, the reference risk attribute data is associated with personal basic data, financial situation and investment experience, each sample data set contains a plurality of parameter values corresponding to M characteristic parameters related to the target parameter, the M characteristic The parameters are related to fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior and credit card consumption behavior; (B) use machine learning algorithms to analyze and calculate these sample data sets to establish a corresponding target parameter. a scoring model; (C) feeding the historical financial behavior data into the scoring model to estimate a model score; and (D) generating the client based on at least the model score and a reference score corresponding to the reference risk attribute data The risk score corresponding to this target parameter.
本發明對於基金商品的投資風險評分方法,還包含以下步驟:(E)輸出對應於該客戶且含有該風險評分的風險評估結果。The investment risk scoring method for fund commodities of the present invention further includes the following steps: (E) outputting a risk assessment result corresponding to the client and containing the risk score.
本發明對於基金商品的投資風險評分方法中,在步驟(B)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 investment risk scoring method for fund commodities of the present invention, in step (B), the machine learning algorithm includes a random forest algorithm, the scoring model includes a plurality of decision trees, and each decision tree is selected from m The corresponding feature parameters of the M feature parameters are subjected to decision calculus and m<M.
本發明對於基金商品的投資風險評分方法中,在步驟(B)中,該目標參數為配置於基金商品所佔的資產比例。In the investment risk scoring method for fund commodities of the present invention, in step (B), the target parameter is the proportion of assets allocated to fund commodities.
本發明對於基金商品的投資風險評分方法中,在步驟(D)中,該電腦系統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 investment risk scoring method for fund commodities of the present invention, in step (D), the computer system further generates the risk according to a predetermined first weight and a predetermined second weight assigned to the reference score and the model score respectively Score, so that the risk score=(the reference score×the predetermined first weight)+(the model score×the predetermined second weight).
於是,本發明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 commodities provided by the present invention is used for risk assessment when a client invests in fund commodities, 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、數位瀏覽行為和信用卡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、數位瀏覽行為和信用卡消費行為相關聯。The data server operates to collect the client's historical financial behavior data and reference risk attribute data, as well as a plurality of 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 spending behavior. The reference risk attribute information is associated with personal basic information, financial situation and investment experience. Each sample data set includes a plurality of parameter values respectively corresponding to M characteristic parameters related to a target parameter, and 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. link.
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 based on at least the model score and a reference score corresponding to the reference risk attribute data , which generates the risk score for the client corresponding to the target parameter.
本發明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 investment risk scoring system for fund commodities of the present invention, the scoring server further includes an output module. The input module is connected to the scoring module and operates to output a risk assessment result corresponding to the client and containing the risk score.
本發明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 investment risk scoring system for fund commodities of the present invention, the machine learning algorithm includes a random forest algorithm, the scoring model includes a plurality of decision trees, and each decision tree is selected from the corresponding M characteristic parameters according to the corresponding The characteristic parameters are used for decision calculus and m<M.
本發明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 investment risk scoring system for fund commodities of the present invention, the target parameter is the proportion of assets allocated to fund commodities.
本發明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 investment risk scoring system for fund commodities of the present invention, 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).
本發明的功效在於: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/或信用卡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 effect of the present invention is: according to the historical financial behavior data associated with the customer's fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior and/or credit card consumption behavior, and use the machine learning algorithm to establish corresponding targets. The model score estimated by the scoring model of the parameters; then the risk score generated according to the model score and the reference score corresponding to the reference risk attribute data of the customer, compared with the score obtained by the existing risk scoring method, can fully and truly reflect the customer As for the risk appetite that fund commodities can bear, this risk score can provide customers with follow-up fund management and/or investment planning services on the premise of effectively avoiding investment risks.
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
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 collecting data such as Information about the (to be scored) customers of the banking institution's financial services organization, and all the information needed 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、一評分模組22及一輸出模組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
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
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、數位瀏覽行為和信用卡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、數位瀏覽行為和信用卡消費行為相關聯。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 store The collected historical financial behavior data, the reference risk attribute data and the sample data sets are sent to the
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信用卡的相關資料(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,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個與信用卡消費行為相關聯之「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 information about the fund commodities that the client has purchased and their profits and losses (such as the average holding days of the fund commodities, the type of the preferred fund commodities, the maximum loss amount, and the degree of volatility of the return). ), other financial products issued by the banking institution and the relevant information of the account balance of the financial account held by the customer, the customer's browsing footprint on the service website provided by the banking institution such as fund wealth management (for example, long-term and short-term browsing information about the number of times and the time of each visit), as well as information about the customer's use of the credit card issued by the banking institution (for example, the card swipe status and whether the revolving rate is used). The reference risk attribute data is, for example, a risk attribute questionnaire answered by the customer through manual filling or input before the customer first subscribes for fund commodities. The content of the risk attribute questionnaire may include, for example, basic information (such as age, occupation), Financial background information (such as income, source of funds) and investment experience data (such as purchased financial products), and according to the content of the risk attribute questionnaire through a predetermined scoring method, a reference score of the customer can be obtained. In this embodiment, the target parameter is, for example, "the proportion of assets allocated to fund commodities", and M=35, for example, so each sample data set may include, for example, 35 parameter values, including: 16 and fund "The number of bond funds held in the past two years", "the number of stock funds held in the past two years", "the number of multi-asset funds held in the past two years", "the number of The number of money-based funds held, the number of balanced funds held in the past two years, the number of fund-of-funds held in the past two years, the number of ETF funds held in the past two years, and the average subscription amount ", "Average subscription amount to total assets", "Average holding days", "Subscription times as a percentage of all transactions", "Subscription times as a percentage of all transactions", "Subscription times/redemption times", Parameter values of characteristic parameters such as "Maximum Profit Amount", "Maximum Profit Amount-Maximum Loss Amount", "Assets Under Management (AUM) Ranking"; 7 "average historical trading funds" associated with the fund's profit and loss records Rate of Return", "Volatility of the average rate of return of historical ETFs", "Number of fund products held in history", "Volatility of historically largest loss rate of return", "Historical largest loss amount", "Historical largest loss amount" The parameter values of characteristic parameters such as volatility” and “profit factor”; 5 “Taiwan dollar living balance”, “Taiwan dollar fixed deposit balance”, “check deposit balance”, “foreign currency living deposit” related to other asset status The parameter values of characteristic parameters such as balance” and “foreign currency fixed deposit balance”; 4 parameters related to digital browsing behaviors are “number of visits to the fund wealth management network in the last month”, “the average stay time in the fund wealth management network in the last month”, “ The parameter values of the characteristic parameters such as the number of visits to the fund wealth management network in the last five months", "the average stay in the fund wealth management network in the last five months"; and the three "credit limit/annual income", " Whether to use the parameter value of characteristic parameters such as Revolving Interest Rate and Average Credit Limit Utilization Rate.
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
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分裂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
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
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
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
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 the allocation of fund commodities (such as the ratio of stocks and debts) in the subsequent fund management or investment planning according to the risk score of the customer.
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、數位瀏覽行為和信用卡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,本發明投資風險評分系統100確實能達成本發明的目的。To sum up, since the scoring model is trained using the sample data set associated with fund transaction behavior, fund profit and loss records, other asset status, digital browsing behavior and credit card consumption behavior, the obtained model score can be realistic. It reflects the client's risk appetite for fund commodities. Therefore, the risk score obtained by combining the (objective) model score with the (subjective) reference score, compared with the score obtained by the existing risk scoring method, can provide customers with follow-up fund management and financing under the premise of effectively avoiding investment risks. / or investment planning services. Therefore, the investment
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.
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 Modules 22: Scoring Module 23: Output module S21-S25: 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是一方塊圖,示例地說明本發明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,示例地說明本發明實施例所建立的一評分模型的架構。Other features and effects of the present invention will be clearly presented in the embodiments with reference to the drawings, wherein: FIG. 1 is a block diagram illustrating the structure of an investment risk scoring system for fund commodities according to an embodiment of the present invention; FIG. 2 is a flow chart illustrating how the embodiment performs an investment risk scoring procedure for a client who wants to invest in fund commodities; and FIG. 3 is a schematic diagram illustrating an example of a scoring model established by an 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 Modules
22:評分模組 22: Scoring Module
23:輸出模組 23: Output module
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